{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 作业一"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5, 10,  9])"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.array([[1.2,1.5,1.8],\n",
    "            [1.3,1.4,1.9],\n",
    "            [1.1,1.6,1.7]])\n",
    "Y = np.array([5,10,9]).T\n",
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "##### 1).循环方式实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 0 ns\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "everyday_total = []\n",
    "for days in X:\n",
    "    sum = 0\n",
    "    index = 0\n",
    "    for item in days:\n",
    "        sum += item * Y[index]\n",
    "        index = index+1\n",
    "    everyday_total.append(sum)\n",
    "everyday_total"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2)矩阵点乘方式实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.34 µs ± 250 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([37.2, 37.6, 36.8])"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%timeit everyday_total = np.dot(X,Y)\n",
    "everyday_total"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 作业二"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 9, 6, 1, 1, 2, 8, 7, 3, 5, 6, 3, 5, 3, 5, 8, 8, 2, 8, 1, 7, 8,\n",
       "       7, 2, 1, 2, 9, 9, 4, 9])"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(1)\n",
    "X = np.random.randint(1,10,size=30)\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1)将X处理为3列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6, 9, 6],\n",
       "       [1, 1, 2],\n",
       "       [8, 7, 3],\n",
       "       [5, 6, 3],\n",
       "       [5, 3, 5],\n",
       "       [8, 8, 2],\n",
       "       [8, 1, 7],\n",
       "       [8, 7, 2],\n",
       "       [1, 2, 9],\n",
       "       [9, 4, 9]])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = X.reshape(-1,3)\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2)将第三列中小于等于3的修改为0、大于3且小于等于6的修改为1、大于6的修改为2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6, 9, 1],\n",
       "       [1, 1, 0],\n",
       "       [8, 7, 0],\n",
       "       [5, 6, 0],\n",
       "       [5, 3, 1],\n",
       "       [8, 8, 0],\n",
       "       [8, 1, 2],\n",
       "       [8, 7, 0],\n",
       "       [1, 2, 2],\n",
       "       [9, 4, 2]])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for index in range(len(X)):\n",
    "    if X[index,2] <= 3:\n",
    "        X[index,2] = 0\n",
    "    if X[index,2] >3 and X[index,2] <= 6:\n",
    "        X[index,2] = 1\n",
    "    if X[index,2] > 6:\n",
    "        X[index,2] = 2\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 3)分离样本和特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6, 9],\n",
       "       [1, 1],\n",
       "       [8, 7],\n",
       "       [5, 6],\n",
       "       [5, 3],\n",
       "       [8, 8],\n",
       "       [8, 1],\n",
       "       [8, 7],\n",
       "       [1, 2],\n",
       "       [9, 4]])"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train = X[:,:2]\n",
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1],\n",
       "       [0],\n",
       "       [0],\n",
       "       [0],\n",
       "       [1],\n",
       "       [0],\n",
       "       [2],\n",
       "       [0],\n",
       "       [2],\n",
       "       [2]])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train = X[:,2:3]\n",
    "y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### 4)通过 y_train 中的数据，分离出 X_train 中的 3 个分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1],\n",
       "       [8, 7],\n",
       "       [5, 6],\n",
       "       [8, 8],\n",
       "       [8, 7]])"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取指定类型的样本数据\n",
    "def getdata(type):\n",
    "    classify_l = []\n",
    "    classify = (X[:,2:3] == type)\n",
    "    for index in range(len(classify)):\n",
    "        if classify[index]:\n",
    "            classify_l.append(x_train[index][0:2])\n",
    "    return classify_l\n",
    "\n",
    "# 分类为0的样本\n",
    "classify_0 = np.array(getdata(0))\n",
    "classify_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6, 9],\n",
       "       [5, 3]])"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 分类为1的样本\n",
    "classify_1 = np.array(getdata(1))\n",
    "classify_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[8, 1],\n",
       "       [1, 2],\n",
       "       [9, 4]])"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 分类为2的样本\n",
    "classify_2 = np.array(getdata(2))\n",
    "classify_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 作业三"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019162542</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162742</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162808</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0                       1  2        3       4       5      6   7  \\\n",
       "0  2019162542  /front-api/bill/create  8  1057.31   88.75  177.72  132.0  60   \n",
       "1      162644  /front-api/bill/create  5   749.12  103.79  240.38  149.0  60   \n",
       "2      162742  /front-api/bill/create  5   845.84  136.31  225.73  169.0  60   \n",
       "3      162808  /front-api/bill/create  9  1305.52   90.12  196.61  145.0  60   \n",
       "4      162943  /front-api/bill/create  3   568.89  138.45  232.02  189.0  60   \n",
       "\n",
       "                     8  \n",
       "0  2018-11-01 00:00:07  \n",
       "1  2018-11-01 00:01:07  \n",
       "2  2018-11-01 00:02:07  \n",
       "3  2018-11-01 00:03:07  \n",
       "4  2018-11-01 00:04:07  "
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./log.txt',header = None,sep = '\\t')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1)给数据加上列名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>api</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>create_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019162542</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162742</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162808</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>162967</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>521.28</td>\n",
       "      <td>80.64</td>\n",
       "      <td>126.17</td>\n",
       "      <td>104.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:05:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>163048</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>464.84</td>\n",
       "      <td>115.97</td>\n",
       "      <td>224.42</td>\n",
       "      <td>154.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:06:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>163207</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>2</td>\n",
       "      <td>337.58</td>\n",
       "      <td>75.58</td>\n",
       "      <td>262.00</td>\n",
       "      <td>168.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:07:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>163290</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>773.22</td>\n",
       "      <td>107.14</td>\n",
       "      <td>207.33</td>\n",
       "      <td>154.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:08:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>163382</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>4</td>\n",
       "      <td>669.66</td>\n",
       "      <td>140.26</td>\n",
       "      <td>225.21</td>\n",
       "      <td>167.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:09:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>163474</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>2</td>\n",
       "      <td>284.78</td>\n",
       "      <td>137.23</td>\n",
       "      <td>147.55</td>\n",
       "      <td>142.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:10:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>163559</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>2</td>\n",
       "      <td>281.44</td>\n",
       "      <td>107.79</td>\n",
       "      <td>173.65</td>\n",
       "      <td>140.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:11:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>163738</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>744.09</td>\n",
       "      <td>124.09</td>\n",
       "      <td>184.19</td>\n",
       "      <td>148.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:13:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>163817</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>502.55</td>\n",
       "      <td>124.74</td>\n",
       "      <td>228.34</td>\n",
       "      <td>167.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:14:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>163856</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>4</td>\n",
       "      <td>606.09</td>\n",
       "      <td>110.09</td>\n",
       "      <td>219.45</td>\n",
       "      <td>151.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:15:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>163999</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>1</td>\n",
       "      <td>189.27</td>\n",
       "      <td>189.27</td>\n",
       "      <td>189.27</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:16:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>164086</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>2</td>\n",
       "      <td>259.86</td>\n",
       "      <td>109.71</td>\n",
       "      <td>150.15</td>\n",
       "      <td>129.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:17:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>164127</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>4</td>\n",
       "      <td>679.70</td>\n",
       "      <td>131.87</td>\n",
       "      <td>215.20</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:18:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>164222</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>431.32</td>\n",
       "      <td>86.83</td>\n",
       "      <td>240.35</td>\n",
       "      <td>143.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:19:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>164334</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>2</td>\n",
       "      <td>312.04</td>\n",
       "      <td>143.40</td>\n",
       "      <td>168.64</td>\n",
       "      <td>156.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:20:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>164449</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>1</td>\n",
       "      <td>131.97</td>\n",
       "      <td>131.97</td>\n",
       "      <td>131.97</td>\n",
       "      <td>131.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:21:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>164500</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>587.98</td>\n",
       "      <td>133.60</td>\n",
       "      <td>299.42</td>\n",
       "      <td>195.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:22:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>164555</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>765.89</td>\n",
       "      <td>86.26</td>\n",
       "      <td>215.45</td>\n",
       "      <td>153.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:23:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>164625</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>2</td>\n",
       "      <td>339.82</td>\n",
       "      <td>117.91</td>\n",
       "      <td>221.91</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:24:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>164727</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>4</td>\n",
       "      <td>515.23</td>\n",
       "      <td>120.84</td>\n",
       "      <td>142.90</td>\n",
       "      <td>128.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:25:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>164812</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>580.40</td>\n",
       "      <td>118.73</td>\n",
       "      <td>240.84</td>\n",
       "      <td>193.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:26:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>164860</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>471.82</td>\n",
       "      <td>104.81</td>\n",
       "      <td>200.54</td>\n",
       "      <td>157.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:27:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>165034</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>2</td>\n",
       "      <td>316.66</td>\n",
       "      <td>99.38</td>\n",
       "      <td>217.28</td>\n",
       "      <td>158.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:29:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>165136</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>4</td>\n",
       "      <td>786.39</td>\n",
       "      <td>142.04</td>\n",
       "      <td>233.86</td>\n",
       "      <td>196.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:30:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>165186</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>4</td>\n",
       "      <td>665.86</td>\n",
       "      <td>127.24</td>\n",
       "      <td>212.55</td>\n",
       "      <td>166.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:31:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179466</th>\n",
       "      <td>13436928</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>11</td>\n",
       "      <td>1749.38</td>\n",
       "      <td>109.36</td>\n",
       "      <td>219.32</td>\n",
       "      <td>159.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 22:41:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179467</th>\n",
       "      <td>13436998</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>14</td>\n",
       "      <td>3621.72</td>\n",
       "      <td>103.97</td>\n",
       "      <td>1194.12</td>\n",
       "      <td>258.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 22:42:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179468</th>\n",
       "      <td>13437067</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>1692.43</td>\n",
       "      <td>88.15</td>\n",
       "      <td>493.56</td>\n",
       "      <td>211.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 22:43:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179469</th>\n",
       "      <td>13437178</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>2496.29</td>\n",
       "      <td>98.70</td>\n",
       "      <td>946.82</td>\n",
       "      <td>312.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 22:44:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179470</th>\n",
       "      <td>13437254</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>4</td>\n",
       "      <td>1004.29</td>\n",
       "      <td>159.22</td>\n",
       "      <td>342.42</td>\n",
       "      <td>251.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 22:45:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179471</th>\n",
       "      <td>13437310</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>1127.33</td>\n",
       "      <td>99.60</td>\n",
       "      <td>252.70</td>\n",
       "      <td>140.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 22:46:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179472</th>\n",
       "      <td>13437403</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>11</td>\n",
       "      <td>2621.67</td>\n",
       "      <td>103.73</td>\n",
       "      <td>532.72</td>\n",
       "      <td>238.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 22:47:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179473</th>\n",
       "      <td>13437462</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>1672.94</td>\n",
       "      <td>118.61</td>\n",
       "      <td>328.94</td>\n",
       "      <td>209.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 22:48:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179474</th>\n",
       "      <td>13437529</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>9</td>\n",
       "      <td>1766.83</td>\n",
       "      <td>86.40</td>\n",
       "      <td>290.11</td>\n",
       "      <td>196.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 22:49:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179475</th>\n",
       "      <td>13437605</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>13</td>\n",
       "      <td>3354.25</td>\n",
       "      <td>109.35</td>\n",
       "      <td>870.09</td>\n",
       "      <td>258.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 22:50:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179476</th>\n",
       "      <td>13437674</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>6</td>\n",
       "      <td>1508.23</td>\n",
       "      <td>151.89</td>\n",
       "      <td>528.19</td>\n",
       "      <td>251.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 22:51:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179477</th>\n",
       "      <td>13437793</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>11</td>\n",
       "      <td>1702.00</td>\n",
       "      <td>88.86</td>\n",
       "      <td>232.60</td>\n",
       "      <td>154.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 22:52:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179478</th>\n",
       "      <td>13437841</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>13</td>\n",
       "      <td>2463.45</td>\n",
       "      <td>72.93</td>\n",
       "      <td>331.06</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 22:53:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179479</th>\n",
       "      <td>13437931</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>6</td>\n",
       "      <td>1525.68</td>\n",
       "      <td>123.02</td>\n",
       "      <td>476.26</td>\n",
       "      <td>254.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 22:54:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179480</th>\n",
       "      <td>13438006</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>13</td>\n",
       "      <td>2556.67</td>\n",
       "      <td>85.51</td>\n",
       "      <td>383.73</td>\n",
       "      <td>196.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 22:55:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179481</th>\n",
       "      <td>13438081</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>6</td>\n",
       "      <td>1784.40</td>\n",
       "      <td>229.68</td>\n",
       "      <td>476.04</td>\n",
       "      <td>297.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 22:56:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179482</th>\n",
       "      <td>13438148</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>6</td>\n",
       "      <td>3338.22</td>\n",
       "      <td>110.07</td>\n",
       "      <td>1818.86</td>\n",
       "      <td>556.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 22:57:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179483</th>\n",
       "      <td>13438231</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>15</td>\n",
       "      <td>4589.76</td>\n",
       "      <td>129.28</td>\n",
       "      <td>1051.45</td>\n",
       "      <td>305.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 22:58:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179484</th>\n",
       "      <td>13438301</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>6</td>\n",
       "      <td>1068.02</td>\n",
       "      <td>111.77</td>\n",
       "      <td>389.24</td>\n",
       "      <td>178.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 22:59:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179485</th>\n",
       "      <td>13438352</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>579.21</td>\n",
       "      <td>73.64</td>\n",
       "      <td>155.20</td>\n",
       "      <td>115.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 23:00:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179486</th>\n",
       "      <td>13438410</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>11</td>\n",
       "      <td>3005.36</td>\n",
       "      <td>109.35</td>\n",
       "      <td>543.06</td>\n",
       "      <td>273.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 23:01:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179487</th>\n",
       "      <td>13438531</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>1324.52</td>\n",
       "      <td>63.97</td>\n",
       "      <td>335.66</td>\n",
       "      <td>165.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 23:02:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179488</th>\n",
       "      <td>13438548</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>2568.12</td>\n",
       "      <td>79.89</td>\n",
       "      <td>1027.96</td>\n",
       "      <td>321.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 23:03:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179489</th>\n",
       "      <td>13438652</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>10</td>\n",
       "      <td>2903.91</td>\n",
       "      <td>125.55</td>\n",
       "      <td>883.17</td>\n",
       "      <td>290.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 23:04:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179490</th>\n",
       "      <td>13438689</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>10</td>\n",
       "      <td>2533.43</td>\n",
       "      <td>155.12</td>\n",
       "      <td>359.90</td>\n",
       "      <td>253.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 23:05:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179491</th>\n",
       "      <td>13438800</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>11</td>\n",
       "      <td>2783.48</td>\n",
       "      <td>99.24</td>\n",
       "      <td>489.90</td>\n",
       "      <td>253.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 23:06:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179492</th>\n",
       "      <td>13438866</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>10</td>\n",
       "      <td>1951.10</td>\n",
       "      <td>85.37</td>\n",
       "      <td>529.51</td>\n",
       "      <td>195.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 23:07:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179493</th>\n",
       "      <td>13438917</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>494.17</td>\n",
       "      <td>103.95</td>\n",
       "      <td>211.47</td>\n",
       "      <td>164.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 23:08:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179494</th>\n",
       "      <td>13438981</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>9</td>\n",
       "      <td>1798.28</td>\n",
       "      <td>101.11</td>\n",
       "      <td>433.30</td>\n",
       "      <td>199.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 23:09:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179495</th>\n",
       "      <td>13439086</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>6</td>\n",
       "      <td>1017.97</td>\n",
       "      <td>74.45</td>\n",
       "      <td>298.97</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 23:10:21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>179496 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                id                     api  count  res_time_sum  res_time_min  \\\n",
       "0       2019162542  /front-api/bill/create      8       1057.31         88.75   \n",
       "1           162644  /front-api/bill/create      5        749.12        103.79   \n",
       "2           162742  /front-api/bill/create      5        845.84        136.31   \n",
       "3           162808  /front-api/bill/create      9       1305.52         90.12   \n",
       "4           162943  /front-api/bill/create      3        568.89        138.45   \n",
       "5           162967  /front-api/bill/create      5        521.28         80.64   \n",
       "6           163048  /front-api/bill/create      3        464.84        115.97   \n",
       "7           163207  /front-api/bill/create      2        337.58         75.58   \n",
       "8           163290  /front-api/bill/create      5        773.22        107.14   \n",
       "9           163382  /front-api/bill/create      4        669.66        140.26   \n",
       "10          163474  /front-api/bill/create      2        284.78        137.23   \n",
       "11          163559  /front-api/bill/create      2        281.44        107.79   \n",
       "12          163738  /front-api/bill/create      5        744.09        124.09   \n",
       "13          163817  /front-api/bill/create      3        502.55        124.74   \n",
       "14          163856  /front-api/bill/create      4        606.09        110.09   \n",
       "15          163999  /front-api/bill/create      1        189.27        189.27   \n",
       "16          164086  /front-api/bill/create      2        259.86        109.71   \n",
       "17          164127  /front-api/bill/create      4        679.70        131.87   \n",
       "18          164222  /front-api/bill/create      3        431.32         86.83   \n",
       "19          164334  /front-api/bill/create      2        312.04        143.40   \n",
       "20          164449  /front-api/bill/create      1        131.97        131.97   \n",
       "21          164500  /front-api/bill/create      3        587.98        133.60   \n",
       "22          164555  /front-api/bill/create      5        765.89         86.26   \n",
       "23          164625  /front-api/bill/create      2        339.82        117.91   \n",
       "24          164727  /front-api/bill/create      4        515.23        120.84   \n",
       "25          164812  /front-api/bill/create      3        580.40        118.73   \n",
       "26          164860  /front-api/bill/create      3        471.82        104.81   \n",
       "27          165034  /front-api/bill/create      2        316.66         99.38   \n",
       "28          165136  /front-api/bill/create      4        786.39        142.04   \n",
       "29          165186  /front-api/bill/create      4        665.86        127.24   \n",
       "...            ...                     ...    ...           ...           ...   \n",
       "179466    13436928  /front-api/bill/create     11       1749.38        109.36   \n",
       "179467    13436998  /front-api/bill/create     14       3621.72        103.97   \n",
       "179468    13437067  /front-api/bill/create      8       1692.43         88.15   \n",
       "179469    13437178  /front-api/bill/create      8       2496.29         98.70   \n",
       "179470    13437254  /front-api/bill/create      4       1004.29        159.22   \n",
       "179471    13437310  /front-api/bill/create      8       1127.33         99.60   \n",
       "179472    13437403  /front-api/bill/create     11       2621.67        103.73   \n",
       "179473    13437462  /front-api/bill/create      8       1672.94        118.61   \n",
       "179474    13437529  /front-api/bill/create      9       1766.83         86.40   \n",
       "179475    13437605  /front-api/bill/create     13       3354.25        109.35   \n",
       "179476    13437674  /front-api/bill/create      6       1508.23        151.89   \n",
       "179477    13437793  /front-api/bill/create     11       1702.00         88.86   \n",
       "179478    13437841  /front-api/bill/create     13       2463.45         72.93   \n",
       "179479    13437931  /front-api/bill/create      6       1525.68        123.02   \n",
       "179480    13438006  /front-api/bill/create     13       2556.67         85.51   \n",
       "179481    13438081  /front-api/bill/create      6       1784.40        229.68   \n",
       "179482    13438148  /front-api/bill/create      6       3338.22        110.07   \n",
       "179483    13438231  /front-api/bill/create     15       4589.76        129.28   \n",
       "179484    13438301  /front-api/bill/create      6       1068.02        111.77   \n",
       "179485    13438352  /front-api/bill/create      5        579.21         73.64   \n",
       "179486    13438410  /front-api/bill/create     11       3005.36        109.35   \n",
       "179487    13438531  /front-api/bill/create      8       1324.52         63.97   \n",
       "179488    13438548  /front-api/bill/create      8       2568.12         79.89   \n",
       "179489    13438652  /front-api/bill/create     10       2903.91        125.55   \n",
       "179490    13438689  /front-api/bill/create     10       2533.43        155.12   \n",
       "179491    13438800  /front-api/bill/create     11       2783.48         99.24   \n",
       "179492    13438866  /front-api/bill/create     10       1951.10         85.37   \n",
       "179493    13438917  /front-api/bill/create      3        494.17        103.95   \n",
       "179494    13438981  /front-api/bill/create      9       1798.28        101.11   \n",
       "179495    13439086  /front-api/bill/create      6       1017.97         74.45   \n",
       "\n",
       "        res_time_max  res_time_avg  interval            create_at  \n",
       "0             177.72         132.0        60  2018-11-01 00:00:07  \n",
       "1             240.38         149.0        60  2018-11-01 00:01:07  \n",
       "2             225.73         169.0        60  2018-11-01 00:02:07  \n",
       "3             196.61         145.0        60  2018-11-01 00:03:07  \n",
       "4             232.02         189.0        60  2018-11-01 00:04:07  \n",
       "5             126.17         104.0        60  2018-11-01 00:05:07  \n",
       "6             224.42         154.0        60  2018-11-01 00:06:07  \n",
       "7             262.00         168.0        60  2018-11-01 00:07:07  \n",
       "8             207.33         154.0        60  2018-11-01 00:08:07  \n",
       "9             225.21         167.0        60  2018-11-01 00:09:07  \n",
       "10            147.55         142.0        60  2018-11-01 00:10:07  \n",
       "11            173.65         140.0        60  2018-11-01 00:11:07  \n",
       "12            184.19         148.0        60  2018-11-01 00:13:07  \n",
       "13            228.34         167.0        60  2018-11-01 00:14:07  \n",
       "14            219.45         151.0        60  2018-11-01 00:15:07  \n",
       "15            189.27         189.0        60  2018-11-01 00:16:07  \n",
       "16            150.15         129.0        60  2018-11-01 00:17:07  \n",
       "17            215.20         169.0        60  2018-11-01 00:18:07  \n",
       "18            240.35         143.0        60  2018-11-01 00:19:07  \n",
       "19            168.64         156.0        60  2018-11-01 00:20:07  \n",
       "20            131.97         131.0        60  2018-11-01 00:21:07  \n",
       "21            299.42         195.0        60  2018-11-01 00:22:07  \n",
       "22            215.45         153.0        60  2018-11-01 00:23:07  \n",
       "23            221.91         169.0        60  2018-11-01 00:24:07  \n",
       "24            142.90         128.0        60  2018-11-01 00:25:07  \n",
       "25            240.84         193.0        60  2018-11-01 00:26:07  \n",
       "26            200.54         157.0        60  2018-11-01 00:27:07  \n",
       "27            217.28         158.0        60  2018-11-01 00:29:07  \n",
       "28            233.86         196.0        60  2018-11-01 00:30:07  \n",
       "29            212.55         166.0        60  2018-11-01 00:31:07  \n",
       "...              ...           ...       ...                  ...  \n",
       "179466        219.32         159.0        60  2019-05-30 22:41:21  \n",
       "179467       1194.12         258.0        60  2019-05-30 22:42:21  \n",
       "179468        493.56         211.0        60  2019-05-30 22:43:21  \n",
       "179469        946.82         312.0        60  2019-05-30 22:44:21  \n",
       "179470        342.42         251.0        60  2019-05-30 22:45:21  \n",
       "179471        252.70         140.0        60  2019-05-30 22:46:21  \n",
       "179472        532.72         238.0        60  2019-05-30 22:47:21  \n",
       "179473        328.94         209.0        60  2019-05-30 22:48:21  \n",
       "179474        290.11         196.0        60  2019-05-30 22:49:21  \n",
       "179475        870.09         258.0        60  2019-05-30 22:50:21  \n",
       "179476        528.19         251.0        60  2019-05-30 22:51:21  \n",
       "179477        232.60         154.0        60  2019-05-30 22:52:21  \n",
       "179478        331.06         189.0        60  2019-05-30 22:53:21  \n",
       "179479        476.26         254.0        60  2019-05-30 22:54:21  \n",
       "179480        383.73         196.0        60  2019-05-30 22:55:21  \n",
       "179481        476.04         297.0        60  2019-05-30 22:56:21  \n",
       "179482       1818.86         556.0        60  2019-05-30 22:57:21  \n",
       "179483       1051.45         305.0        60  2019-05-30 22:58:21  \n",
       "179484        389.24         178.0        60  2019-05-30 22:59:21  \n",
       "179485        155.20         115.0        60  2019-05-30 23:00:21  \n",
       "179486        543.06         273.0        60  2019-05-30 23:01:21  \n",
       "179487        335.66         165.0        60  2019-05-30 23:02:21  \n",
       "179488       1027.96         321.0        60  2019-05-30 23:03:21  \n",
       "179489        883.17         290.0        60  2019-05-30 23:04:21  \n",
       "179490        359.90         253.0        60  2019-05-30 23:05:21  \n",
       "179491        489.90         253.0        60  2019-05-30 23:06:21  \n",
       "179492        529.51         195.0        60  2019-05-30 23:07:21  \n",
       "179493        211.47         164.0        60  2019-05-30 23:08:21  \n",
       "179494        433.30         199.0        60  2019-05-30 23:09:21  \n",
       "179495        298.97         169.0        60  2019-05-30 23:10:21  \n",
       "\n",
       "[179496 rows x 9 columns]"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = ['id','api','count','res_time_sum','res_time_min','res_time_max','res_time_avg','interval','create_at']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>api</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>create_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>110415</th>\n",
       "      <td>8171705</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>1</td>\n",
       "      <td>128.02</td>\n",
       "      <td>128.02</td>\n",
       "      <td>128.02</td>\n",
       "      <td>128.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-13 00:50:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44417</th>\n",
       "      <td>3793696</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>6</td>\n",
       "      <td>1300.90</td>\n",
       "      <td>119.18</td>\n",
       "      <td>296.11</td>\n",
       "      <td>216.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-12-22 20:08:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172294</th>\n",
       "      <td>12880165</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>1678.21</td>\n",
       "      <td>132.85</td>\n",
       "      <td>475.91</td>\n",
       "      <td>209.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-22 21:33:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140896</th>\n",
       "      <td>10460431</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>1</td>\n",
       "      <td>189.56</td>\n",
       "      <td>189.56</td>\n",
       "      <td>189.56</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-04-17 11:18:34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91088</th>\n",
       "      <td>6884297</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>468.11</td>\n",
       "      <td>121.39</td>\n",
       "      <td>189.87</td>\n",
       "      <td>156.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-02-16 20:03:17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              id                     api  count  res_time_sum  res_time_min  \\\n",
       "110415   8171705  /front-api/bill/create      1        128.02        128.02   \n",
       "44417    3793696  /front-api/bill/create      6       1300.90        119.18   \n",
       "172294  12880165  /front-api/bill/create      8       1678.21        132.85   \n",
       "140896  10460431  /front-api/bill/create      1        189.56        189.56   \n",
       "91088    6884297  /front-api/bill/create      3        468.11        121.39   \n",
       "\n",
       "        res_time_max  res_time_avg  interval            create_at  \n",
       "110415        128.02         128.0        60  2019-03-13 00:50:56  \n",
       "44417         296.11         216.0        60  2018-12-22 20:08:42  \n",
       "172294        475.91         209.0        60  2019-05-22 21:33:13  \n",
       "140896        189.56         189.0        60  2019-04-17 11:18:34  \n",
       "91088         189.87         156.0        60  2019-02-16 20:03:17  "
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5) # 随机采样，多次执行数据不一样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(179496, 9)"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape # 查看行列信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                int64\n",
       "api              object\n",
       "count             int64\n",
       "res_time_sum    float64\n",
       "res_time_min    float64\n",
       "res_time_max    float64\n",
       "res_time_avg    float64\n",
       "interval          int64\n",
       "create_at        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 179496 entries, 0 to 179495\n",
      "Data columns (total 9 columns):\n",
      "id              179496 non-null int64\n",
      "api             179496 non-null object\n",
      "count           179496 non-null int64\n",
      "res_time_sum    179496 non-null float64\n",
      "res_time_min    179496 non-null float64\n",
      "res_time_max    179496 non-null float64\n",
      "res_time_avg    179496 non-null float64\n",
      "interval        179496 non-null int64\n",
      "create_at       179496 non-null object\n",
      "dtypes: float64(4), int64(3), object(2)\n",
      "memory usage: 12.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info() # 查看内存占用空间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                     179496\n",
       "unique                         1\n",
       "top       /front-api/bill/create\n",
       "freq                      179496\n",
       "Name: api, dtype: object"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['api'].describe() # 查看api这一列的信息 ，由此可见api这一列数据都相同"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>create_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019162542</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162742</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162808</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>162967</td>\n",
       "      <td>5</td>\n",
       "      <td>521.28</td>\n",
       "      <td>80.64</td>\n",
       "      <td>126.17</td>\n",
       "      <td>104.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:05:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>163048</td>\n",
       "      <td>3</td>\n",
       "      <td>464.84</td>\n",
       "      <td>115.97</td>\n",
       "      <td>224.42</td>\n",
       "      <td>154.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:06:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>163207</td>\n",
       "      <td>2</td>\n",
       "      <td>337.58</td>\n",
       "      <td>75.58</td>\n",
       "      <td>262.00</td>\n",
       "      <td>168.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:07:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>163290</td>\n",
       "      <td>5</td>\n",
       "      <td>773.22</td>\n",
       "      <td>107.14</td>\n",
       "      <td>207.33</td>\n",
       "      <td>154.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:08:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>163382</td>\n",
       "      <td>4</td>\n",
       "      <td>669.66</td>\n",
       "      <td>140.26</td>\n",
       "      <td>225.21</td>\n",
       "      <td>167.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:09:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>163474</td>\n",
       "      <td>2</td>\n",
       "      <td>284.78</td>\n",
       "      <td>137.23</td>\n",
       "      <td>147.55</td>\n",
       "      <td>142.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:10:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>163559</td>\n",
       "      <td>2</td>\n",
       "      <td>281.44</td>\n",
       "      <td>107.79</td>\n",
       "      <td>173.65</td>\n",
       "      <td>140.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:11:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>163738</td>\n",
       "      <td>5</td>\n",
       "      <td>744.09</td>\n",
       "      <td>124.09</td>\n",
       "      <td>184.19</td>\n",
       "      <td>148.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:13:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>163817</td>\n",
       "      <td>3</td>\n",
       "      <td>502.55</td>\n",
       "      <td>124.74</td>\n",
       "      <td>228.34</td>\n",
       "      <td>167.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:14:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>163856</td>\n",
       "      <td>4</td>\n",
       "      <td>606.09</td>\n",
       "      <td>110.09</td>\n",
       "      <td>219.45</td>\n",
       "      <td>151.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:15:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>163999</td>\n",
       "      <td>1</td>\n",
       "      <td>189.27</td>\n",
       "      <td>189.27</td>\n",
       "      <td>189.27</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:16:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>164086</td>\n",
       "      <td>2</td>\n",
       "      <td>259.86</td>\n",
       "      <td>109.71</td>\n",
       "      <td>150.15</td>\n",
       "      <td>129.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:17:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>164127</td>\n",
       "      <td>4</td>\n",
       "      <td>679.70</td>\n",
       "      <td>131.87</td>\n",
       "      <td>215.20</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:18:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>164222</td>\n",
       "      <td>3</td>\n",
       "      <td>431.32</td>\n",
       "      <td>86.83</td>\n",
       "      <td>240.35</td>\n",
       "      <td>143.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:19:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>164334</td>\n",
       "      <td>2</td>\n",
       "      <td>312.04</td>\n",
       "      <td>143.40</td>\n",
       "      <td>168.64</td>\n",
       "      <td>156.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:20:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>164449</td>\n",
       "      <td>1</td>\n",
       "      <td>131.97</td>\n",
       "      <td>131.97</td>\n",
       "      <td>131.97</td>\n",
       "      <td>131.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:21:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>164500</td>\n",
       "      <td>3</td>\n",
       "      <td>587.98</td>\n",
       "      <td>133.60</td>\n",
       "      <td>299.42</td>\n",
       "      <td>195.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:22:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>164555</td>\n",
       "      <td>5</td>\n",
       "      <td>765.89</td>\n",
       "      <td>86.26</td>\n",
       "      <td>215.45</td>\n",
       "      <td>153.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:23:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>164625</td>\n",
       "      <td>2</td>\n",
       "      <td>339.82</td>\n",
       "      <td>117.91</td>\n",
       "      <td>221.91</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:24:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>164727</td>\n",
       "      <td>4</td>\n",
       "      <td>515.23</td>\n",
       "      <td>120.84</td>\n",
       "      <td>142.90</td>\n",
       "      <td>128.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:25:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>164812</td>\n",
       "      <td>3</td>\n",
       "      <td>580.40</td>\n",
       "      <td>118.73</td>\n",
       "      <td>240.84</td>\n",
       "      <td>193.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:26:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>164860</td>\n",
       "      <td>3</td>\n",
       "      <td>471.82</td>\n",
       "      <td>104.81</td>\n",
       "      <td>200.54</td>\n",
       "      <td>157.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:27:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>165034</td>\n",
       "      <td>2</td>\n",
       "      <td>316.66</td>\n",
       "      <td>99.38</td>\n",
       "      <td>217.28</td>\n",
       "      <td>158.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:29:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>165136</td>\n",
       "      <td>4</td>\n",
       "      <td>786.39</td>\n",
       "      <td>142.04</td>\n",
       "      <td>233.86</td>\n",
       "      <td>196.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:30:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>165186</td>\n",
       "      <td>4</td>\n",
       "      <td>665.86</td>\n",
       "      <td>127.24</td>\n",
       "      <td>212.55</td>\n",
       "      <td>166.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:31:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179466</th>\n",
       "      <td>13436928</td>\n",
       "      <td>11</td>\n",
       "      <td>1749.38</td>\n",
       "      <td>109.36</td>\n",
       "      <td>219.32</td>\n",
       "      <td>159.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 22:41:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179467</th>\n",
       "      <td>13436998</td>\n",
       "      <td>14</td>\n",
       "      <td>3621.72</td>\n",
       "      <td>103.97</td>\n",
       "      <td>1194.12</td>\n",
       "      <td>258.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 22:42:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179468</th>\n",
       "      <td>13437067</td>\n",
       "      <td>8</td>\n",
       "      <td>1692.43</td>\n",
       "      <td>88.15</td>\n",
       "      <td>493.56</td>\n",
       "      <td>211.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 22:43:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179469</th>\n",
       "      <td>13437178</td>\n",
       "      <td>8</td>\n",
       "      <td>2496.29</td>\n",
       "      <td>98.70</td>\n",
       "      <td>946.82</td>\n",
       "      <td>312.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 22:44:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179470</th>\n",
       "      <td>13437254</td>\n",
       "      <td>4</td>\n",
       "      <td>1004.29</td>\n",
       "      <td>159.22</td>\n",
       "      <td>342.42</td>\n",
       "      <td>251.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 22:45:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179471</th>\n",
       "      <td>13437310</td>\n",
       "      <td>8</td>\n",
       "      <td>1127.33</td>\n",
       "      <td>99.60</td>\n",
       "      <td>252.70</td>\n",
       "      <td>140.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 22:46:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179472</th>\n",
       "      <td>13437403</td>\n",
       "      <td>11</td>\n",
       "      <td>2621.67</td>\n",
       "      <td>103.73</td>\n",
       "      <td>532.72</td>\n",
       "      <td>238.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 22:47:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179473</th>\n",
       "      <td>13437462</td>\n",
       "      <td>8</td>\n",
       "      <td>1672.94</td>\n",
       "      <td>118.61</td>\n",
       "      <td>328.94</td>\n",
       "      <td>209.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 22:48:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179474</th>\n",
       "      <td>13437529</td>\n",
       "      <td>9</td>\n",
       "      <td>1766.83</td>\n",
       "      <td>86.40</td>\n",
       "      <td>290.11</td>\n",
       "      <td>196.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 22:49:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179475</th>\n",
       "      <td>13437605</td>\n",
       "      <td>13</td>\n",
       "      <td>3354.25</td>\n",
       "      <td>109.35</td>\n",
       "      <td>870.09</td>\n",
       "      <td>258.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 22:50:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179476</th>\n",
       "      <td>13437674</td>\n",
       "      <td>6</td>\n",
       "      <td>1508.23</td>\n",
       "      <td>151.89</td>\n",
       "      <td>528.19</td>\n",
       "      <td>251.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 22:51:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179477</th>\n",
       "      <td>13437793</td>\n",
       "      <td>11</td>\n",
       "      <td>1702.00</td>\n",
       "      <td>88.86</td>\n",
       "      <td>232.60</td>\n",
       "      <td>154.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 22:52:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179478</th>\n",
       "      <td>13437841</td>\n",
       "      <td>13</td>\n",
       "      <td>2463.45</td>\n",
       "      <td>72.93</td>\n",
       "      <td>331.06</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 22:53:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179479</th>\n",
       "      <td>13437931</td>\n",
       "      <td>6</td>\n",
       "      <td>1525.68</td>\n",
       "      <td>123.02</td>\n",
       "      <td>476.26</td>\n",
       "      <td>254.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 22:54:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179480</th>\n",
       "      <td>13438006</td>\n",
       "      <td>13</td>\n",
       "      <td>2556.67</td>\n",
       "      <td>85.51</td>\n",
       "      <td>383.73</td>\n",
       "      <td>196.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 22:55:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179481</th>\n",
       "      <td>13438081</td>\n",
       "      <td>6</td>\n",
       "      <td>1784.40</td>\n",
       "      <td>229.68</td>\n",
       "      <td>476.04</td>\n",
       "      <td>297.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 22:56:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179482</th>\n",
       "      <td>13438148</td>\n",
       "      <td>6</td>\n",
       "      <td>3338.22</td>\n",
       "      <td>110.07</td>\n",
       "      <td>1818.86</td>\n",
       "      <td>556.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 22:57:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179483</th>\n",
       "      <td>13438231</td>\n",
       "      <td>15</td>\n",
       "      <td>4589.76</td>\n",
       "      <td>129.28</td>\n",
       "      <td>1051.45</td>\n",
       "      <td>305.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 22:58:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179484</th>\n",
       "      <td>13438301</td>\n",
       "      <td>6</td>\n",
       "      <td>1068.02</td>\n",
       "      <td>111.77</td>\n",
       "      <td>389.24</td>\n",
       "      <td>178.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 22:59:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179485</th>\n",
       "      <td>13438352</td>\n",
       "      <td>5</td>\n",
       "      <td>579.21</td>\n",
       "      <td>73.64</td>\n",
       "      <td>155.20</td>\n",
       "      <td>115.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 23:00:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179486</th>\n",
       "      <td>13438410</td>\n",
       "      <td>11</td>\n",
       "      <td>3005.36</td>\n",
       "      <td>109.35</td>\n",
       "      <td>543.06</td>\n",
       "      <td>273.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 23:01:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179487</th>\n",
       "      <td>13438531</td>\n",
       "      <td>8</td>\n",
       "      <td>1324.52</td>\n",
       "      <td>63.97</td>\n",
       "      <td>335.66</td>\n",
       "      <td>165.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 23:02:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179488</th>\n",
       "      <td>13438548</td>\n",
       "      <td>8</td>\n",
       "      <td>2568.12</td>\n",
       "      <td>79.89</td>\n",
       "      <td>1027.96</td>\n",
       "      <td>321.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 23:03:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179489</th>\n",
       "      <td>13438652</td>\n",
       "      <td>10</td>\n",
       "      <td>2903.91</td>\n",
       "      <td>125.55</td>\n",
       "      <td>883.17</td>\n",
       "      <td>290.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 23:04:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179490</th>\n",
       "      <td>13438689</td>\n",
       "      <td>10</td>\n",
       "      <td>2533.43</td>\n",
       "      <td>155.12</td>\n",
       "      <td>359.90</td>\n",
       "      <td>253.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 23:05:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179491</th>\n",
       "      <td>13438800</td>\n",
       "      <td>11</td>\n",
       "      <td>2783.48</td>\n",
       "      <td>99.24</td>\n",
       "      <td>489.90</td>\n",
       "      <td>253.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 23:06:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179492</th>\n",
       "      <td>13438866</td>\n",
       "      <td>10</td>\n",
       "      <td>1951.10</td>\n",
       "      <td>85.37</td>\n",
       "      <td>529.51</td>\n",
       "      <td>195.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 23:07:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179493</th>\n",
       "      <td>13438917</td>\n",
       "      <td>3</td>\n",
       "      <td>494.17</td>\n",
       "      <td>103.95</td>\n",
       "      <td>211.47</td>\n",
       "      <td>164.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 23:08:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179494</th>\n",
       "      <td>13438981</td>\n",
       "      <td>9</td>\n",
       "      <td>1798.28</td>\n",
       "      <td>101.11</td>\n",
       "      <td>433.30</td>\n",
       "      <td>199.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 23:09:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179495</th>\n",
       "      <td>13439086</td>\n",
       "      <td>6</td>\n",
       "      <td>1017.97</td>\n",
       "      <td>74.45</td>\n",
       "      <td>298.97</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-30 23:10:21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>179496 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                id  count  res_time_sum  res_time_min  res_time_max  \\\n",
       "0       2019162542      8       1057.31         88.75        177.72   \n",
       "1           162644      5        749.12        103.79        240.38   \n",
       "2           162742      5        845.84        136.31        225.73   \n",
       "3           162808      9       1305.52         90.12        196.61   \n",
       "4           162943      3        568.89        138.45        232.02   \n",
       "5           162967      5        521.28         80.64        126.17   \n",
       "6           163048      3        464.84        115.97        224.42   \n",
       "7           163207      2        337.58         75.58        262.00   \n",
       "8           163290      5        773.22        107.14        207.33   \n",
       "9           163382      4        669.66        140.26        225.21   \n",
       "10          163474      2        284.78        137.23        147.55   \n",
       "11          163559      2        281.44        107.79        173.65   \n",
       "12          163738      5        744.09        124.09        184.19   \n",
       "13          163817      3        502.55        124.74        228.34   \n",
       "14          163856      4        606.09        110.09        219.45   \n",
       "15          163999      1        189.27        189.27        189.27   \n",
       "16          164086      2        259.86        109.71        150.15   \n",
       "17          164127      4        679.70        131.87        215.20   \n",
       "18          164222      3        431.32         86.83        240.35   \n",
       "19          164334      2        312.04        143.40        168.64   \n",
       "20          164449      1        131.97        131.97        131.97   \n",
       "21          164500      3        587.98        133.60        299.42   \n",
       "22          164555      5        765.89         86.26        215.45   \n",
       "23          164625      2        339.82        117.91        221.91   \n",
       "24          164727      4        515.23        120.84        142.90   \n",
       "25          164812      3        580.40        118.73        240.84   \n",
       "26          164860      3        471.82        104.81        200.54   \n",
       "27          165034      2        316.66         99.38        217.28   \n",
       "28          165136      4        786.39        142.04        233.86   \n",
       "29          165186      4        665.86        127.24        212.55   \n",
       "...            ...    ...           ...           ...           ...   \n",
       "179466    13436928     11       1749.38        109.36        219.32   \n",
       "179467    13436998     14       3621.72        103.97       1194.12   \n",
       "179468    13437067      8       1692.43         88.15        493.56   \n",
       "179469    13437178      8       2496.29         98.70        946.82   \n",
       "179470    13437254      4       1004.29        159.22        342.42   \n",
       "179471    13437310      8       1127.33         99.60        252.70   \n",
       "179472    13437403     11       2621.67        103.73        532.72   \n",
       "179473    13437462      8       1672.94        118.61        328.94   \n",
       "179474    13437529      9       1766.83         86.40        290.11   \n",
       "179475    13437605     13       3354.25        109.35        870.09   \n",
       "179476    13437674      6       1508.23        151.89        528.19   \n",
       "179477    13437793     11       1702.00         88.86        232.60   \n",
       "179478    13437841     13       2463.45         72.93        331.06   \n",
       "179479    13437931      6       1525.68        123.02        476.26   \n",
       "179480    13438006     13       2556.67         85.51        383.73   \n",
       "179481    13438081      6       1784.40        229.68        476.04   \n",
       "179482    13438148      6       3338.22        110.07       1818.86   \n",
       "179483    13438231     15       4589.76        129.28       1051.45   \n",
       "179484    13438301      6       1068.02        111.77        389.24   \n",
       "179485    13438352      5        579.21         73.64        155.20   \n",
       "179486    13438410     11       3005.36        109.35        543.06   \n",
       "179487    13438531      8       1324.52         63.97        335.66   \n",
       "179488    13438548      8       2568.12         79.89       1027.96   \n",
       "179489    13438652     10       2903.91        125.55        883.17   \n",
       "179490    13438689     10       2533.43        155.12        359.90   \n",
       "179491    13438800     11       2783.48         99.24        489.90   \n",
       "179492    13438866     10       1951.10         85.37        529.51   \n",
       "179493    13438917      3        494.17        103.95        211.47   \n",
       "179494    13438981      9       1798.28        101.11        433.30   \n",
       "179495    13439086      6       1017.97         74.45        298.97   \n",
       "\n",
       "        res_time_avg  interval            create_at  \n",
       "0              132.0        60  2018-11-01 00:00:07  \n",
       "1              149.0        60  2018-11-01 00:01:07  \n",
       "2              169.0        60  2018-11-01 00:02:07  \n",
       "3              145.0        60  2018-11-01 00:03:07  \n",
       "4              189.0        60  2018-11-01 00:04:07  \n",
       "5              104.0        60  2018-11-01 00:05:07  \n",
       "6              154.0        60  2018-11-01 00:06:07  \n",
       "7              168.0        60  2018-11-01 00:07:07  \n",
       "8              154.0        60  2018-11-01 00:08:07  \n",
       "9              167.0        60  2018-11-01 00:09:07  \n",
       "10             142.0        60  2018-11-01 00:10:07  \n",
       "11             140.0        60  2018-11-01 00:11:07  \n",
       "12             148.0        60  2018-11-01 00:13:07  \n",
       "13             167.0        60  2018-11-01 00:14:07  \n",
       "14             151.0        60  2018-11-01 00:15:07  \n",
       "15             189.0        60  2018-11-01 00:16:07  \n",
       "16             129.0        60  2018-11-01 00:17:07  \n",
       "17             169.0        60  2018-11-01 00:18:07  \n",
       "18             143.0        60  2018-11-01 00:19:07  \n",
       "19             156.0        60  2018-11-01 00:20:07  \n",
       "20             131.0        60  2018-11-01 00:21:07  \n",
       "21             195.0        60  2018-11-01 00:22:07  \n",
       "22             153.0        60  2018-11-01 00:23:07  \n",
       "23             169.0        60  2018-11-01 00:24:07  \n",
       "24             128.0        60  2018-11-01 00:25:07  \n",
       "25             193.0        60  2018-11-01 00:26:07  \n",
       "26             157.0        60  2018-11-01 00:27:07  \n",
       "27             158.0        60  2018-11-01 00:29:07  \n",
       "28             196.0        60  2018-11-01 00:30:07  \n",
       "29             166.0        60  2018-11-01 00:31:07  \n",
       "...              ...       ...                  ...  \n",
       "179466         159.0        60  2019-05-30 22:41:21  \n",
       "179467         258.0        60  2019-05-30 22:42:21  \n",
       "179468         211.0        60  2019-05-30 22:43:21  \n",
       "179469         312.0        60  2019-05-30 22:44:21  \n",
       "179470         251.0        60  2019-05-30 22:45:21  \n",
       "179471         140.0        60  2019-05-30 22:46:21  \n",
       "179472         238.0        60  2019-05-30 22:47:21  \n",
       "179473         209.0        60  2019-05-30 22:48:21  \n",
       "179474         196.0        60  2019-05-30 22:49:21  \n",
       "179475         258.0        60  2019-05-30 22:50:21  \n",
       "179476         251.0        60  2019-05-30 22:51:21  \n",
       "179477         154.0        60  2019-05-30 22:52:21  \n",
       "179478         189.0        60  2019-05-30 22:53:21  \n",
       "179479         254.0        60  2019-05-30 22:54:21  \n",
       "179480         196.0        60  2019-05-30 22:55:21  \n",
       "179481         297.0        60  2019-05-30 22:56:21  \n",
       "179482         556.0        60  2019-05-30 22:57:21  \n",
       "179483         305.0        60  2019-05-30 22:58:21  \n",
       "179484         178.0        60  2019-05-30 22:59:21  \n",
       "179485         115.0        60  2019-05-30 23:00:21  \n",
       "179486         273.0        60  2019-05-30 23:01:21  \n",
       "179487         165.0        60  2019-05-30 23:02:21  \n",
       "179488         321.0        60  2019-05-30 23:03:21  \n",
       "179489         290.0        60  2019-05-30 23:04:21  \n",
       "179490         253.0        60  2019-05-30 23:05:21  \n",
       "179491         253.0        60  2019-05-30 23:06:21  \n",
       "179492         195.0        60  2019-05-30 23:07:21  \n",
       "179493         164.0        60  2019-05-30 23:08:21  \n",
       "179494         199.0        60  2019-05-30 23:09:21  \n",
       "179495         169.0        60  2019-05-30 23:10:21  \n",
       "\n",
       "[179496 rows x 8 columns]"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop('api',axis = 1) # 删除pai这一列数据\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 179496 entries, 0 to 179495\n",
      "Data columns (total 8 columns):\n",
      "id              179496 non-null int64\n",
      "count           179496 non-null int64\n",
      "res_time_sum    179496 non-null float64\n",
      "res_time_min    179496 non-null float64\n",
      "res_time_max    179496 non-null float64\n",
      "res_time_avg    179496 non-null float64\n",
      "interval        179496 non-null int64\n",
      "create_at       179496 non-null object\n",
      "dtypes: float64(4), int64(3), object(1)\n",
      "memory usage: 11.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info() # 删除api一列后内存减少1.3M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                  179496\n",
       "unique                 179496\n",
       "top       2019-05-08 13:59:57\n",
       "freq                        1\n",
       "Name: create_at, dtype: object"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['create_at'].describe() # 可见create_at一列数据均不相同"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>create_at</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>153089</th>\n",
       "      <td>11406128</td>\n",
       "      <td>6</td>\n",
       "      <td>2105.08</td>\n",
       "      <td>125.74</td>\n",
       "      <td>992.46</td>\n",
       "      <td>350.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:00:48</td>\n",
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       "    <tr>\n",
       "      <th>153090</th>\n",
       "      <td>11406236</td>\n",
       "      <td>7</td>\n",
       "      <td>2579.11</td>\n",
       "      <td>76.55</td>\n",
       "      <td>987.47</td>\n",
       "      <td>368.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:01:48</td>\n",
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       "    <tr>\n",
       "      <th>153091</th>\n",
       "      <td>11406347</td>\n",
       "      <td>7</td>\n",
       "      <td>1277.79</td>\n",
       "      <td>109.65</td>\n",
       "      <td>236.73</td>\n",
       "      <td>182.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:02:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153092</th>\n",
       "      <td>11406446</td>\n",
       "      <td>7</td>\n",
       "      <td>2137.20</td>\n",
       "      <td>131.55</td>\n",
       "      <td>920.52</td>\n",
       "      <td>305.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:03:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153093</th>\n",
       "      <td>11406488</td>\n",
       "      <td>13</td>\n",
       "      <td>2948.70</td>\n",
       "      <td>86.42</td>\n",
       "      <td>491.31</td>\n",
       "      <td>226.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:04:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153094</th>\n",
       "      <td>11406599</td>\n",
       "      <td>6</td>\n",
       "      <td>2463.78</td>\n",
       "      <td>137.75</td>\n",
       "      <td>1445.82</td>\n",
       "      <td>410.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:05:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153095</th>\n",
       "      <td>11406661</td>\n",
       "      <td>6</td>\n",
       "      <td>2875.67</td>\n",
       "      <td>166.32</td>\n",
       "      <td>1304.41</td>\n",
       "      <td>479.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:06:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153096</th>\n",
       "      <td>11406751</td>\n",
       "      <td>8</td>\n",
       "      <td>1764.17</td>\n",
       "      <td>93.63</td>\n",
       "      <td>425.96</td>\n",
       "      <td>220.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:07:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153097</th>\n",
       "      <td>11406812</td>\n",
       "      <td>8</td>\n",
       "      <td>2577.12</td>\n",
       "      <td>148.68</td>\n",
       "      <td>864.03</td>\n",
       "      <td>322.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:08:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153098</th>\n",
       "      <td>11406929</td>\n",
       "      <td>5</td>\n",
       "      <td>929.82</td>\n",
       "      <td>67.42</td>\n",
       "      <td>413.51</td>\n",
       "      <td>185.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:09:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153099</th>\n",
       "      <td>11407005</td>\n",
       "      <td>4</td>\n",
       "      <td>912.60</td>\n",
       "      <td>171.17</td>\n",
       "      <td>297.85</td>\n",
       "      <td>228.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:10:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153100</th>\n",
       "      <td>11407047</td>\n",
       "      <td>2</td>\n",
       "      <td>279.56</td>\n",
       "      <td>123.47</td>\n",
       "      <td>156.09</td>\n",
       "      <td>139.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:11:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153101</th>\n",
       "      <td>11407133</td>\n",
       "      <td>4</td>\n",
       "      <td>714.73</td>\n",
       "      <td>125.50</td>\n",
       "      <td>226.84</td>\n",
       "      <td>178.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:12:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153102</th>\n",
       "      <td>11407234</td>\n",
       "      <td>5</td>\n",
       "      <td>1285.32</td>\n",
       "      <td>81.12</td>\n",
       "      <td>436.79</td>\n",
       "      <td>257.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:13:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153103</th>\n",
       "      <td>11407282</td>\n",
       "      <td>6</td>\n",
       "      <td>1425.18</td>\n",
       "      <td>99.28</td>\n",
       "      <td>571.42</td>\n",
       "      <td>237.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:14:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153104</th>\n",
       "      <td>11407386</td>\n",
       "      <td>5</td>\n",
       "      <td>947.69</td>\n",
       "      <td>97.91</td>\n",
       "      <td>313.41</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:15:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153105</th>\n",
       "      <td>11407436</td>\n",
       "      <td>4</td>\n",
       "      <td>1000.06</td>\n",
       "      <td>157.33</td>\n",
       "      <td>335.86</td>\n",
       "      <td>250.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:16:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153106</th>\n",
       "      <td>11407531</td>\n",
       "      <td>2</td>\n",
       "      <td>279.14</td>\n",
       "      <td>117.30</td>\n",
       "      <td>161.84</td>\n",
       "      <td>139.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:17:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153107</th>\n",
       "      <td>11407611</td>\n",
       "      <td>7</td>\n",
       "      <td>994.75</td>\n",
       "      <td>73.33</td>\n",
       "      <td>229.60</td>\n",
       "      <td>142.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:18:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153108</th>\n",
       "      <td>11407632</td>\n",
       "      <td>8</td>\n",
       "      <td>2207.46</td>\n",
       "      <td>76.31</td>\n",
       "      <td>1114.91</td>\n",
       "      <td>275.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:19:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153109</th>\n",
       "      <td>11407730</td>\n",
       "      <td>6</td>\n",
       "      <td>1244.12</td>\n",
       "      <td>119.18</td>\n",
       "      <td>400.02</td>\n",
       "      <td>207.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:20:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153110</th>\n",
       "      <td>11407845</td>\n",
       "      <td>4</td>\n",
       "      <td>892.43</td>\n",
       "      <td>103.66</td>\n",
       "      <td>374.82</td>\n",
       "      <td>223.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:21:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153111</th>\n",
       "      <td>11407897</td>\n",
       "      <td>4</td>\n",
       "      <td>1093.26</td>\n",
       "      <td>66.57</td>\n",
       "      <td>434.01</td>\n",
       "      <td>273.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:22:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153112</th>\n",
       "      <td>11407980</td>\n",
       "      <td>6</td>\n",
       "      <td>1116.52</td>\n",
       "      <td>89.45</td>\n",
       "      <td>485.38</td>\n",
       "      <td>186.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:23:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153113</th>\n",
       "      <td>11408036</td>\n",
       "      <td>6</td>\n",
       "      <td>770.21</td>\n",
       "      <td>77.44</td>\n",
       "      <td>217.87</td>\n",
       "      <td>128.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:24:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153114</th>\n",
       "      <td>11408107</td>\n",
       "      <td>6</td>\n",
       "      <td>1308.97</td>\n",
       "      <td>89.86</td>\n",
       "      <td>399.41</td>\n",
       "      <td>218.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:25:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153115</th>\n",
       "      <td>11408194</td>\n",
       "      <td>5</td>\n",
       "      <td>848.25</td>\n",
       "      <td>108.51</td>\n",
       "      <td>260.88</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:26:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153116</th>\n",
       "      <td>11408253</td>\n",
       "      <td>5</td>\n",
       "      <td>2407.06</td>\n",
       "      <td>90.05</td>\n",
       "      <td>1186.62</td>\n",
       "      <td>481.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:27:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153117</th>\n",
       "      <td>11408357</td>\n",
       "      <td>4</td>\n",
       "      <td>710.47</td>\n",
       "      <td>163.89</td>\n",
       "      <td>191.80</td>\n",
       "      <td>177.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:28:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153118</th>\n",
       "      <td>11408389</td>\n",
       "      <td>7</td>\n",
       "      <td>1675.60</td>\n",
       "      <td>110.26</td>\n",
       "      <td>619.54</td>\n",
       "      <td>239.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 00:29:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153943</th>\n",
       "      <td>11473695</td>\n",
       "      <td>3</td>\n",
       "      <td>471.28</td>\n",
       "      <td>86.32</td>\n",
       "      <td>194.36</td>\n",
       "      <td>157.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:30:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153944</th>\n",
       "      <td>11473734</td>\n",
       "      <td>9</td>\n",
       "      <td>1753.33</td>\n",
       "      <td>81.64</td>\n",
       "      <td>545.84</td>\n",
       "      <td>194.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:31:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153945</th>\n",
       "      <td>11473812</td>\n",
       "      <td>3</td>\n",
       "      <td>566.92</td>\n",
       "      <td>166.21</td>\n",
       "      <td>213.47</td>\n",
       "      <td>188.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:32:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153946</th>\n",
       "      <td>11473844</td>\n",
       "      <td>2</td>\n",
       "      <td>258.84</td>\n",
       "      <td>65.36</td>\n",
       "      <td>193.48</td>\n",
       "      <td>129.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:33:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153947</th>\n",
       "      <td>11473942</td>\n",
       "      <td>2</td>\n",
       "      <td>300.97</td>\n",
       "      <td>138.49</td>\n",
       "      <td>162.48</td>\n",
       "      <td>150.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:34:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153948</th>\n",
       "      <td>11474015</td>\n",
       "      <td>6</td>\n",
       "      <td>792.55</td>\n",
       "      <td>69.46</td>\n",
       "      <td>239.17</td>\n",
       "      <td>132.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:35:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153949</th>\n",
       "      <td>11474088</td>\n",
       "      <td>6</td>\n",
       "      <td>1157.81</td>\n",
       "      <td>124.12</td>\n",
       "      <td>423.91</td>\n",
       "      <td>192.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:36:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153950</th>\n",
       "      <td>11474163</td>\n",
       "      <td>2</td>\n",
       "      <td>433.06</td>\n",
       "      <td>98.41</td>\n",
       "      <td>334.65</td>\n",
       "      <td>216.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:37:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153951</th>\n",
       "      <td>11474223</td>\n",
       "      <td>4</td>\n",
       "      <td>425.51</td>\n",
       "      <td>75.69</td>\n",
       "      <td>144.11</td>\n",
       "      <td>106.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:38:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153952</th>\n",
       "      <td>11474299</td>\n",
       "      <td>4</td>\n",
       "      <td>604.55</td>\n",
       "      <td>103.00</td>\n",
       "      <td>191.69</td>\n",
       "      <td>151.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:39:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153953</th>\n",
       "      <td>11474340</td>\n",
       "      <td>4</td>\n",
       "      <td>599.14</td>\n",
       "      <td>141.13</td>\n",
       "      <td>162.50</td>\n",
       "      <td>149.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:40:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153954</th>\n",
       "      <td>11474412</td>\n",
       "      <td>3</td>\n",
       "      <td>519.14</td>\n",
       "      <td>130.28</td>\n",
       "      <td>219.06</td>\n",
       "      <td>173.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:41:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153955</th>\n",
       "      <td>11474510</td>\n",
       "      <td>1</td>\n",
       "      <td>336.79</td>\n",
       "      <td>336.79</td>\n",
       "      <td>336.79</td>\n",
       "      <td>336.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:42:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153956</th>\n",
       "      <td>11474559</td>\n",
       "      <td>8</td>\n",
       "      <td>1741.96</td>\n",
       "      <td>83.68</td>\n",
       "      <td>592.15</td>\n",
       "      <td>217.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:43:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153957</th>\n",
       "      <td>11474630</td>\n",
       "      <td>5</td>\n",
       "      <td>573.94</td>\n",
       "      <td>75.98</td>\n",
       "      <td>160.20</td>\n",
       "      <td>114.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:44:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153958</th>\n",
       "      <td>11474719</td>\n",
       "      <td>5</td>\n",
       "      <td>1221.15</td>\n",
       "      <td>74.16</td>\n",
       "      <td>726.07</td>\n",
       "      <td>244.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:45:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153959</th>\n",
       "      <td>11474783</td>\n",
       "      <td>7</td>\n",
       "      <td>775.40</td>\n",
       "      <td>69.56</td>\n",
       "      <td>165.25</td>\n",
       "      <td>110.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:46:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153960</th>\n",
       "      <td>11474860</td>\n",
       "      <td>5</td>\n",
       "      <td>1109.98</td>\n",
       "      <td>114.90</td>\n",
       "      <td>406.98</td>\n",
       "      <td>221.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:47:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153961</th>\n",
       "      <td>11474885</td>\n",
       "      <td>5</td>\n",
       "      <td>563.23</td>\n",
       "      <td>83.24</td>\n",
       "      <td>171.42</td>\n",
       "      <td>112.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:48:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153962</th>\n",
       "      <td>11474974</td>\n",
       "      <td>3</td>\n",
       "      <td>351.08</td>\n",
       "      <td>69.84</td>\n",
       "      <td>148.27</td>\n",
       "      <td>117.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:49:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153963</th>\n",
       "      <td>11475041</td>\n",
       "      <td>4</td>\n",
       "      <td>609.49</td>\n",
       "      <td>89.03</td>\n",
       "      <td>235.60</td>\n",
       "      <td>152.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:50:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153964</th>\n",
       "      <td>11475066</td>\n",
       "      <td>4</td>\n",
       "      <td>1285.34</td>\n",
       "      <td>154.31</td>\n",
       "      <td>538.34</td>\n",
       "      <td>321.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:51:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153965</th>\n",
       "      <td>11475136</td>\n",
       "      <td>4</td>\n",
       "      <td>884.68</td>\n",
       "      <td>111.59</td>\n",
       "      <td>468.82</td>\n",
       "      <td>221.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:52:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153966</th>\n",
       "      <td>11475226</td>\n",
       "      <td>7</td>\n",
       "      <td>1377.46</td>\n",
       "      <td>133.20</td>\n",
       "      <td>248.60</td>\n",
       "      <td>196.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:53:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153967</th>\n",
       "      <td>11475311</td>\n",
       "      <td>4</td>\n",
       "      <td>656.67</td>\n",
       "      <td>126.56</td>\n",
       "      <td>243.48</td>\n",
       "      <td>164.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:54:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153968</th>\n",
       "      <td>11475363</td>\n",
       "      <td>6</td>\n",
       "      <td>1083.97</td>\n",
       "      <td>70.85</td>\n",
       "      <td>262.22</td>\n",
       "      <td>180.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:55:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153969</th>\n",
       "      <td>11475483</td>\n",
       "      <td>4</td>\n",
       "      <td>840.00</td>\n",
       "      <td>117.31</td>\n",
       "      <td>382.63</td>\n",
       "      <td>210.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:56:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153970</th>\n",
       "      <td>11475550</td>\n",
       "      <td>2</td>\n",
       "      <td>295.51</td>\n",
       "      <td>101.71</td>\n",
       "      <td>193.80</td>\n",
       "      <td>147.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:57:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153971</th>\n",
       "      <td>11475597</td>\n",
       "      <td>2</td>\n",
       "      <td>431.99</td>\n",
       "      <td>84.43</td>\n",
       "      <td>347.56</td>\n",
       "      <td>215.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:58:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153972</th>\n",
       "      <td>11475664</td>\n",
       "      <td>3</td>\n",
       "      <td>428.84</td>\n",
       "      <td>103.58</td>\n",
       "      <td>206.57</td>\n",
       "      <td>142.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-01 23:59:49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>884 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id  count  res_time_sum  res_time_min  res_time_max  \\\n",
       "153089  11406128      6       2105.08        125.74        992.46   \n",
       "153090  11406236      7       2579.11         76.55        987.47   \n",
       "153091  11406347      7       1277.79        109.65        236.73   \n",
       "153092  11406446      7       2137.20        131.55        920.52   \n",
       "153093  11406488     13       2948.70         86.42        491.31   \n",
       "153094  11406599      6       2463.78        137.75       1445.82   \n",
       "153095  11406661      6       2875.67        166.32       1304.41   \n",
       "153096  11406751      8       1764.17         93.63        425.96   \n",
       "153097  11406812      8       2577.12        148.68        864.03   \n",
       "153098  11406929      5        929.82         67.42        413.51   \n",
       "153099  11407005      4        912.60        171.17        297.85   \n",
       "153100  11407047      2        279.56        123.47        156.09   \n",
       "153101  11407133      4        714.73        125.50        226.84   \n",
       "153102  11407234      5       1285.32         81.12        436.79   \n",
       "153103  11407282      6       1425.18         99.28        571.42   \n",
       "153104  11407386      5        947.69         97.91        313.41   \n",
       "153105  11407436      4       1000.06        157.33        335.86   \n",
       "153106  11407531      2        279.14        117.30        161.84   \n",
       "153107  11407611      7        994.75         73.33        229.60   \n",
       "153108  11407632      8       2207.46         76.31       1114.91   \n",
       "153109  11407730      6       1244.12        119.18        400.02   \n",
       "153110  11407845      4        892.43        103.66        374.82   \n",
       "153111  11407897      4       1093.26         66.57        434.01   \n",
       "153112  11407980      6       1116.52         89.45        485.38   \n",
       "153113  11408036      6        770.21         77.44        217.87   \n",
       "153114  11408107      6       1308.97         89.86        399.41   \n",
       "153115  11408194      5        848.25        108.51        260.88   \n",
       "153116  11408253      5       2407.06         90.05       1186.62   \n",
       "153117  11408357      4        710.47        163.89        191.80   \n",
       "153118  11408389      7       1675.60        110.26        619.54   \n",
       "...          ...    ...           ...           ...           ...   \n",
       "153943  11473695      3        471.28         86.32        194.36   \n",
       "153944  11473734      9       1753.33         81.64        545.84   \n",
       "153945  11473812      3        566.92        166.21        213.47   \n",
       "153946  11473844      2        258.84         65.36        193.48   \n",
       "153947  11473942      2        300.97        138.49        162.48   \n",
       "153948  11474015      6        792.55         69.46        239.17   \n",
       "153949  11474088      6       1157.81        124.12        423.91   \n",
       "153950  11474163      2        433.06         98.41        334.65   \n",
       "153951  11474223      4        425.51         75.69        144.11   \n",
       "153952  11474299      4        604.55        103.00        191.69   \n",
       "153953  11474340      4        599.14        141.13        162.50   \n",
       "153954  11474412      3        519.14        130.28        219.06   \n",
       "153955  11474510      1        336.79        336.79        336.79   \n",
       "153956  11474559      8       1741.96         83.68        592.15   \n",
       "153957  11474630      5        573.94         75.98        160.20   \n",
       "153958  11474719      5       1221.15         74.16        726.07   \n",
       "153959  11474783      7        775.40         69.56        165.25   \n",
       "153960  11474860      5       1109.98        114.90        406.98   \n",
       "153961  11474885      5        563.23         83.24        171.42   \n",
       "153962  11474974      3        351.08         69.84        148.27   \n",
       "153963  11475041      4        609.49         89.03        235.60   \n",
       "153964  11475066      4       1285.34        154.31        538.34   \n",
       "153965  11475136      4        884.68        111.59        468.82   \n",
       "153966  11475226      7       1377.46        133.20        248.60   \n",
       "153967  11475311      4        656.67        126.56        243.48   \n",
       "153968  11475363      6       1083.97         70.85        262.22   \n",
       "153969  11475483      4        840.00        117.31        382.63   \n",
       "153970  11475550      2        295.51        101.71        193.80   \n",
       "153971  11475597      2        431.99         84.43        347.56   \n",
       "153972  11475664      3        428.84        103.58        206.57   \n",
       "\n",
       "        res_time_avg  interval            create_at  \n",
       "153089         350.0        60  2019-05-01 00:00:48  \n",
       "153090         368.0        60  2019-05-01 00:01:48  \n",
       "153091         182.0        60  2019-05-01 00:02:48  \n",
       "153092         305.0        60  2019-05-01 00:03:48  \n",
       "153093         226.0        60  2019-05-01 00:04:48  \n",
       "153094         410.0        60  2019-05-01 00:05:48  \n",
       "153095         479.0        60  2019-05-01 00:06:48  \n",
       "153096         220.0        60  2019-05-01 00:07:48  \n",
       "153097         322.0        60  2019-05-01 00:08:48  \n",
       "153098         185.0        60  2019-05-01 00:09:48  \n",
       "153099         228.0        60  2019-05-01 00:10:48  \n",
       "153100         139.0        60  2019-05-01 00:11:48  \n",
       "153101         178.0        60  2019-05-01 00:12:48  \n",
       "153102         257.0        60  2019-05-01 00:13:48  \n",
       "153103         237.0        60  2019-05-01 00:14:48  \n",
       "153104         189.0        60  2019-05-01 00:15:48  \n",
       "153105         250.0        60  2019-05-01 00:16:48  \n",
       "153106         139.0        60  2019-05-01 00:17:48  \n",
       "153107         142.0        60  2019-05-01 00:18:48  \n",
       "153108         275.0        60  2019-05-01 00:19:48  \n",
       "153109         207.0        60  2019-05-01 00:20:48  \n",
       "153110         223.0        60  2019-05-01 00:21:48  \n",
       "153111         273.0        60  2019-05-01 00:22:48  \n",
       "153112         186.0        60  2019-05-01 00:23:48  \n",
       "153113         128.0        60  2019-05-01 00:24:48  \n",
       "153114         218.0        60  2019-05-01 00:25:48  \n",
       "153115         169.0        60  2019-05-01 00:26:48  \n",
       "153116         481.0        60  2019-05-01 00:27:48  \n",
       "153117         177.0        60  2019-05-01 00:28:48  \n",
       "153118         239.0        60  2019-05-01 00:29:48  \n",
       "...              ...       ...                  ...  \n",
       "153943         157.0        60  2019-05-01 23:30:49  \n",
       "153944         194.0        60  2019-05-01 23:31:49  \n",
       "153945         188.0        60  2019-05-01 23:32:49  \n",
       "153946         129.0        60  2019-05-01 23:33:49  \n",
       "153947         150.0        60  2019-05-01 23:34:49  \n",
       "153948         132.0        60  2019-05-01 23:35:49  \n",
       "153949         192.0        60  2019-05-01 23:36:49  \n",
       "153950         216.0        60  2019-05-01 23:37:49  \n",
       "153951         106.0        60  2019-05-01 23:38:49  \n",
       "153952         151.0        60  2019-05-01 23:39:49  \n",
       "153953         149.0        60  2019-05-01 23:40:49  \n",
       "153954         173.0        60  2019-05-01 23:41:49  \n",
       "153955         336.0        60  2019-05-01 23:42:49  \n",
       "153956         217.0        60  2019-05-01 23:43:49  \n",
       "153957         114.0        60  2019-05-01 23:44:49  \n",
       "153958         244.0        60  2019-05-01 23:45:49  \n",
       "153959         110.0        60  2019-05-01 23:46:49  \n",
       "153960         221.0        60  2019-05-01 23:47:49  \n",
       "153961         112.0        60  2019-05-01 23:48:49  \n",
       "153962         117.0        60  2019-05-01 23:49:49  \n",
       "153963         152.0        60  2019-05-01 23:50:49  \n",
       "153964         321.0        60  2019-05-01 23:51:49  \n",
       "153965         221.0        60  2019-05-01 23:52:49  \n",
       "153966         196.0        60  2019-05-01 23:53:49  \n",
       "153967         164.0        60  2019-05-01 23:54:49  \n",
       "153968         180.0        60  2019-05-01 23:55:49  \n",
       "153969         210.0        60  2019-05-01 23:56:49  \n",
       "153970         147.0        60  2019-05-01 23:57:49  \n",
       "153971         215.0        60  2019-05-01 23:58:49  \n",
       "153972         142.0        60  2019-05-01 23:59:49  \n",
       "\n",
       "[884 rows x 8 columns]"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看指定时间段内数据\n",
    "df[(df.create_at >= '2019-05-01') & (df.create_at < '2019-05-02')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=179496, step=1)"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index # 查看当前索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['2018-11-01 00:00:07', '2018-11-01 00:01:07', '2018-11-01 00:02:07',\n",
       "       '2018-11-01 00:03:07', '2018-11-01 00:04:07', '2018-11-01 00:05:07',\n",
       "       '2018-11-01 00:06:07', '2018-11-01 00:07:07', '2018-11-01 00:08:07',\n",
       "       '2018-11-01 00:09:07',\n",
       "       ...\n",
       "       '2019-05-30 23:01:21', '2019-05-30 23:02:21', '2019-05-30 23:03:21',\n",
       "       '2019-05-30 23:04:21', '2019-05-30 23:05:21', '2019-05-30 23:06:21',\n",
       "       '2019-05-30 23:07:21', '2019-05-30 23:08:21', '2019-05-30 23:09:21',\n",
       "       '2019-05-30 23:10:21'],\n",
       "      dtype='object', name='create_at', length=179496)"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index = df['create_at'] # 把创建时间设为索引\n",
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-11-01 00:00:07', '2018-11-01 00:01:07',\n",
       "               '2018-11-01 00:02:07', '2018-11-01 00:03:07',\n",
       "               '2018-11-01 00:04:07', '2018-11-01 00:05:07',\n",
       "               '2018-11-01 00:06:07', '2018-11-01 00:07:07',\n",
       "               '2018-11-01 00:08:07', '2018-11-01 00:09:07',\n",
       "               ...\n",
       "               '2019-05-30 23:01:21', '2019-05-30 23:02:21',\n",
       "               '2019-05-30 23:03:21', '2019-05-30 23:04:21',\n",
       "               '2019-05-30 23:05:21', '2019-05-30 23:06:21',\n",
       "               '2019-05-30 23:07:21', '2019-05-30 23:08:21',\n",
       "               '2019-05-30 23:09:21', '2019-05-30 23:10:21'],\n",
       "              dtype='datetime64[ns]', name='create_at', length=179496, freq=None)"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index = pd.to_datetime(df.create_at) # 把索引由字符串转换为datetime对象,方便查找等相关操作\n",
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>create_at</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>create_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-05-02 00:00:49</th>\n",
       "      <td>11475722</td>\n",
       "      <td>2</td>\n",
       "      <td>484.69</td>\n",
       "      <td>101.07</td>\n",
       "      <td>383.62</td>\n",
       "      <td>242.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 00:00:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 00:01:49</th>\n",
       "      <td>11475763</td>\n",
       "      <td>4</td>\n",
       "      <td>801.94</td>\n",
       "      <td>108.22</td>\n",
       "      <td>292.17</td>\n",
       "      <td>200.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 00:01:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 00:02:49</th>\n",
       "      <td>11475867</td>\n",
       "      <td>6</td>\n",
       "      <td>1888.32</td>\n",
       "      <td>99.93</td>\n",
       "      <td>782.74</td>\n",
       "      <td>314.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 00:02:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 00:03:49</th>\n",
       "      <td>11475900</td>\n",
       "      <td>5</td>\n",
       "      <td>858.39</td>\n",
       "      <td>102.00</td>\n",
       "      <td>266.54</td>\n",
       "      <td>171.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 00:03:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 00:04:49</th>\n",
       "      <td>11475991</td>\n",
       "      <td>3</td>\n",
       "      <td>778.11</td>\n",
       "      <td>162.48</td>\n",
       "      <td>373.41</td>\n",
       "      <td>259.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 00:04:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 00:05:49</th>\n",
       "      <td>11476055</td>\n",
       "      <td>3</td>\n",
       "      <td>1140.26</td>\n",
       "      <td>106.67</td>\n",
       "      <td>822.38</td>\n",
       "      <td>380.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 00:05:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 00:06:49</th>\n",
       "      <td>11476142</td>\n",
       "      <td>2</td>\n",
       "      <td>347.17</td>\n",
       "      <td>145.63</td>\n",
       "      <td>201.54</td>\n",
       "      <td>173.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 00:06:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 00:07:49</th>\n",
       "      <td>11476189</td>\n",
       "      <td>2</td>\n",
       "      <td>400.15</td>\n",
       "      <td>129.87</td>\n",
       "      <td>270.28</td>\n",
       "      <td>200.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 00:07:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 00:08:49</th>\n",
       "      <td>11476244</td>\n",
       "      <td>1</td>\n",
       "      <td>89.02</td>\n",
       "      <td>89.02</td>\n",
       "      <td>89.02</td>\n",
       "      <td>89.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 00:08:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 00:10:49</th>\n",
       "      <td>11476380</td>\n",
       "      <td>5</td>\n",
       "      <td>624.60</td>\n",
       "      <td>99.56</td>\n",
       "      <td>170.76</td>\n",
       "      <td>124.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 00:10:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 00:11:49</th>\n",
       "      <td>11476456</td>\n",
       "      <td>2</td>\n",
       "      <td>238.44</td>\n",
       "      <td>93.34</td>\n",
       "      <td>145.10</td>\n",
       "      <td>119.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 00:11:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 00:12:49</th>\n",
       "      <td>11476500</td>\n",
       "      <td>2</td>\n",
       "      <td>517.82</td>\n",
       "      <td>104.31</td>\n",
       "      <td>413.51</td>\n",
       "      <td>258.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 00:12:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 00:13:49</th>\n",
       "      <td>11476565</td>\n",
       "      <td>9</td>\n",
       "      <td>1829.15</td>\n",
       "      <td>76.87</td>\n",
       "      <td>646.42</td>\n",
       "      <td>203.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 00:13:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 00:14:49</th>\n",
       "      <td>11476663</td>\n",
       "      <td>2</td>\n",
       "      <td>551.31</td>\n",
       "      <td>139.07</td>\n",
       "      <td>412.24</td>\n",
       "      <td>275.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 00:14:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 00:15:49</th>\n",
       "      <td>11476732</td>\n",
       "      <td>5</td>\n",
       "      <td>629.29</td>\n",
       "      <td>101.79</td>\n",
       "      <td>196.48</td>\n",
       "      <td>125.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 00:15:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 00:16:49</th>\n",
       "      <td>11476764</td>\n",
       "      <td>3</td>\n",
       "      <td>1219.00</td>\n",
       "      <td>126.32</td>\n",
       "      <td>927.92</td>\n",
       "      <td>406.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 00:16:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 00:17:49</th>\n",
       "      <td>11476854</td>\n",
       "      <td>4</td>\n",
       "      <td>733.00</td>\n",
       "      <td>129.86</td>\n",
       "      <td>224.39</td>\n",
       "      <td>183.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 00:17:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 00:18:49</th>\n",
       "      <td>11476938</td>\n",
       "      <td>3</td>\n",
       "      <td>487.64</td>\n",
       "      <td>89.50</td>\n",
       "      <td>204.78</td>\n",
       "      <td>162.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 00:18:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 00:19:49</th>\n",
       "      <td>11477010</td>\n",
       "      <td>4</td>\n",
       "      <td>863.46</td>\n",
       "      <td>153.05</td>\n",
       "      <td>384.72</td>\n",
       "      <td>215.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 00:19:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 00:20:49</th>\n",
       "      <td>11477077</td>\n",
       "      <td>4</td>\n",
       "      <td>962.74</td>\n",
       "      <td>105.48</td>\n",
       "      <td>487.76</td>\n",
       "      <td>240.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 00:20:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 00:21:49</th>\n",
       "      <td>11477140</td>\n",
       "      <td>4</td>\n",
       "      <td>846.10</td>\n",
       "      <td>115.28</td>\n",
       "      <td>310.30</td>\n",
       "      <td>211.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 00:21:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 00:22:49</th>\n",
       "      <td>11477191</td>\n",
       "      <td>1</td>\n",
       "      <td>253.08</td>\n",
       "      <td>253.08</td>\n",
       "      <td>253.08</td>\n",
       "      <td>253.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 00:22:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 00:23:49</th>\n",
       "      <td>11477237</td>\n",
       "      <td>5</td>\n",
       "      <td>648.09</td>\n",
       "      <td>109.89</td>\n",
       "      <td>140.59</td>\n",
       "      <td>129.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 00:23:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 00:24:49</th>\n",
       "      <td>11477313</td>\n",
       "      <td>4</td>\n",
       "      <td>1115.02</td>\n",
       "      <td>108.05</td>\n",
       "      <td>547.07</td>\n",
       "      <td>278.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 00:24:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 00:25:49</th>\n",
       "      <td>11477373</td>\n",
       "      <td>2</td>\n",
       "      <td>255.23</td>\n",
       "      <td>106.75</td>\n",
       "      <td>148.48</td>\n",
       "      <td>127.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 00:25:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 00:26:49</th>\n",
       "      <td>11477450</td>\n",
       "      <td>2</td>\n",
       "      <td>429.78</td>\n",
       "      <td>173.64</td>\n",
       "      <td>256.14</td>\n",
       "      <td>214.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 00:26:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 00:27:49</th>\n",
       "      <td>11477488</td>\n",
       "      <td>2</td>\n",
       "      <td>402.06</td>\n",
       "      <td>167.55</td>\n",
       "      <td>234.51</td>\n",
       "      <td>201.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 00:27:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 00:28:49</th>\n",
       "      <td>11477539</td>\n",
       "      <td>3</td>\n",
       "      <td>401.34</td>\n",
       "      <td>124.29</td>\n",
       "      <td>143.68</td>\n",
       "      <td>133.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 00:28:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 00:29:49</th>\n",
       "      <td>11477605</td>\n",
       "      <td>3</td>\n",
       "      <td>1354.69</td>\n",
       "      <td>114.69</td>\n",
       "      <td>942.69</td>\n",
       "      <td>451.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 00:29:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 00:30:49</th>\n",
       "      <td>11477677</td>\n",
       "      <td>3</td>\n",
       "      <td>415.81</td>\n",
       "      <td>127.65</td>\n",
       "      <td>150.90</td>\n",
       "      <td>138.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 00:30:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 23:30:51</th>\n",
       "      <td>11539365</td>\n",
       "      <td>7</td>\n",
       "      <td>1265.05</td>\n",
       "      <td>87.51</td>\n",
       "      <td>249.41</td>\n",
       "      <td>180.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 23:30:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 23:31:51</th>\n",
       "      <td>11539471</td>\n",
       "      <td>3</td>\n",
       "      <td>385.11</td>\n",
       "      <td>113.29</td>\n",
       "      <td>151.39</td>\n",
       "      <td>128.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 23:31:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 23:32:51</th>\n",
       "      <td>11539496</td>\n",
       "      <td>4</td>\n",
       "      <td>572.21</td>\n",
       "      <td>106.20</td>\n",
       "      <td>226.00</td>\n",
       "      <td>143.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 23:32:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 23:33:51</th>\n",
       "      <td>11539585</td>\n",
       "      <td>5</td>\n",
       "      <td>757.15</td>\n",
       "      <td>91.95</td>\n",
       "      <td>258.72</td>\n",
       "      <td>151.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 23:33:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 23:34:51</th>\n",
       "      <td>11539680</td>\n",
       "      <td>10</td>\n",
       "      <td>1924.06</td>\n",
       "      <td>86.33</td>\n",
       "      <td>415.48</td>\n",
       "      <td>192.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 23:34:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 23:35:51</th>\n",
       "      <td>11539736</td>\n",
       "      <td>7</td>\n",
       "      <td>1044.32</td>\n",
       "      <td>112.03</td>\n",
       "      <td>201.77</td>\n",
       "      <td>149.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 23:35:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 23:36:51</th>\n",
       "      <td>11539793</td>\n",
       "      <td>7</td>\n",
       "      <td>1255.16</td>\n",
       "      <td>89.74</td>\n",
       "      <td>332.84</td>\n",
       "      <td>179.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 23:36:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 23:37:51</th>\n",
       "      <td>11539856</td>\n",
       "      <td>10</td>\n",
       "      <td>2143.21</td>\n",
       "      <td>125.08</td>\n",
       "      <td>356.68</td>\n",
       "      <td>214.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 23:37:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 23:38:51</th>\n",
       "      <td>11539928</td>\n",
       "      <td>7</td>\n",
       "      <td>1230.97</td>\n",
       "      <td>71.90</td>\n",
       "      <td>453.22</td>\n",
       "      <td>175.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 23:38:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 23:39:51</th>\n",
       "      <td>11540035</td>\n",
       "      <td>4</td>\n",
       "      <td>580.32</td>\n",
       "      <td>102.69</td>\n",
       "      <td>207.32</td>\n",
       "      <td>145.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 23:39:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 23:40:51</th>\n",
       "      <td>11540090</td>\n",
       "      <td>6</td>\n",
       "      <td>1934.47</td>\n",
       "      <td>86.47</td>\n",
       "      <td>1033.02</td>\n",
       "      <td>322.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 23:40:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 23:41:51</th>\n",
       "      <td>11540132</td>\n",
       "      <td>4</td>\n",
       "      <td>510.76</td>\n",
       "      <td>88.82</td>\n",
       "      <td>185.52</td>\n",
       "      <td>127.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 23:41:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 23:42:51</th>\n",
       "      <td>11540224</td>\n",
       "      <td>4</td>\n",
       "      <td>420.63</td>\n",
       "      <td>75.49</td>\n",
       "      <td>137.94</td>\n",
       "      <td>105.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 23:42:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 23:43:51</th>\n",
       "      <td>11540307</td>\n",
       "      <td>3</td>\n",
       "      <td>609.28</td>\n",
       "      <td>75.26</td>\n",
       "      <td>288.71</td>\n",
       "      <td>203.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 23:43:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 23:44:51</th>\n",
       "      <td>11540344</td>\n",
       "      <td>3</td>\n",
       "      <td>642.03</td>\n",
       "      <td>141.71</td>\n",
       "      <td>283.59</td>\n",
       "      <td>214.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 23:44:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 23:45:51</th>\n",
       "      <td>11540408</td>\n",
       "      <td>8</td>\n",
       "      <td>1461.57</td>\n",
       "      <td>71.59</td>\n",
       "      <td>645.84</td>\n",
       "      <td>182.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 23:45:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 23:46:51</th>\n",
       "      <td>11540485</td>\n",
       "      <td>4</td>\n",
       "      <td>997.30</td>\n",
       "      <td>105.76</td>\n",
       "      <td>455.57</td>\n",
       "      <td>249.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 23:46:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 23:47:51</th>\n",
       "      <td>11540550</td>\n",
       "      <td>3</td>\n",
       "      <td>978.67</td>\n",
       "      <td>130.26</td>\n",
       "      <td>649.39</td>\n",
       "      <td>326.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 23:47:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 23:48:51</th>\n",
       "      <td>11540604</td>\n",
       "      <td>4</td>\n",
       "      <td>572.21</td>\n",
       "      <td>79.91</td>\n",
       "      <td>213.63</td>\n",
       "      <td>143.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 23:48:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 23:49:51</th>\n",
       "      <td>11540665</td>\n",
       "      <td>7</td>\n",
       "      <td>1885.74</td>\n",
       "      <td>84.52</td>\n",
       "      <td>654.95</td>\n",
       "      <td>269.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 23:49:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 23:50:51</th>\n",
       "      <td>11540744</td>\n",
       "      <td>3</td>\n",
       "      <td>323.29</td>\n",
       "      <td>100.12</td>\n",
       "      <td>118.35</td>\n",
       "      <td>107.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 23:50:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 23:51:51</th>\n",
       "      <td>11540811</td>\n",
       "      <td>3</td>\n",
       "      <td>296.45</td>\n",
       "      <td>75.38</td>\n",
       "      <td>139.71</td>\n",
       "      <td>98.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 23:51:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 23:52:51</th>\n",
       "      <td>11540875</td>\n",
       "      <td>1</td>\n",
       "      <td>295.96</td>\n",
       "      <td>295.96</td>\n",
       "      <td>295.96</td>\n",
       "      <td>295.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 23:52:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 23:53:51</th>\n",
       "      <td>11540913</td>\n",
       "      <td>6</td>\n",
       "      <td>1045.60</td>\n",
       "      <td>69.12</td>\n",
       "      <td>446.44</td>\n",
       "      <td>174.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 23:53:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 23:54:51</th>\n",
       "      <td>11540995</td>\n",
       "      <td>4</td>\n",
       "      <td>755.00</td>\n",
       "      <td>153.64</td>\n",
       "      <td>238.43</td>\n",
       "      <td>188.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 23:54:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 23:55:51</th>\n",
       "      <td>11541056</td>\n",
       "      <td>2</td>\n",
       "      <td>392.55</td>\n",
       "      <td>84.96</td>\n",
       "      <td>307.59</td>\n",
       "      <td>196.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 23:55:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 23:56:51</th>\n",
       "      <td>11541082</td>\n",
       "      <td>5</td>\n",
       "      <td>1133.19</td>\n",
       "      <td>124.87</td>\n",
       "      <td>318.95</td>\n",
       "      <td>226.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 23:56:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 23:57:51</th>\n",
       "      <td>11541153</td>\n",
       "      <td>1</td>\n",
       "      <td>886.99</td>\n",
       "      <td>886.99</td>\n",
       "      <td>886.99</td>\n",
       "      <td>886.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 23:57:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 23:58:51</th>\n",
       "      <td>11541171</td>\n",
       "      <td>2</td>\n",
       "      <td>259.38</td>\n",
       "      <td>115.29</td>\n",
       "      <td>144.09</td>\n",
       "      <td>129.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 23:58:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 23:59:51</th>\n",
       "      <td>11541226</td>\n",
       "      <td>6</td>\n",
       "      <td>823.94</td>\n",
       "      <td>110.75</td>\n",
       "      <td>226.56</td>\n",
       "      <td>137.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-05-02 23:59:51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>865 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           id  count  res_time_sum  res_time_min  \\\n",
       "create_at                                                          \n",
       "2019-05-02 00:00:49  11475722      2        484.69        101.07   \n",
       "2019-05-02 00:01:49  11475763      4        801.94        108.22   \n",
       "2019-05-02 00:02:49  11475867      6       1888.32         99.93   \n",
       "2019-05-02 00:03:49  11475900      5        858.39        102.00   \n",
       "2019-05-02 00:04:49  11475991      3        778.11        162.48   \n",
       "2019-05-02 00:05:49  11476055      3       1140.26        106.67   \n",
       "2019-05-02 00:06:49  11476142      2        347.17        145.63   \n",
       "2019-05-02 00:07:49  11476189      2        400.15        129.87   \n",
       "2019-05-02 00:08:49  11476244      1         89.02         89.02   \n",
       "2019-05-02 00:10:49  11476380      5        624.60         99.56   \n",
       "2019-05-02 00:11:49  11476456      2        238.44         93.34   \n",
       "2019-05-02 00:12:49  11476500      2        517.82        104.31   \n",
       "2019-05-02 00:13:49  11476565      9       1829.15         76.87   \n",
       "2019-05-02 00:14:49  11476663      2        551.31        139.07   \n",
       "2019-05-02 00:15:49  11476732      5        629.29        101.79   \n",
       "2019-05-02 00:16:49  11476764      3       1219.00        126.32   \n",
       "2019-05-02 00:17:49  11476854      4        733.00        129.86   \n",
       "2019-05-02 00:18:49  11476938      3        487.64         89.50   \n",
       "2019-05-02 00:19:49  11477010      4        863.46        153.05   \n",
       "2019-05-02 00:20:49  11477077      4        962.74        105.48   \n",
       "2019-05-02 00:21:49  11477140      4        846.10        115.28   \n",
       "2019-05-02 00:22:49  11477191      1        253.08        253.08   \n",
       "2019-05-02 00:23:49  11477237      5        648.09        109.89   \n",
       "2019-05-02 00:24:49  11477313      4       1115.02        108.05   \n",
       "2019-05-02 00:25:49  11477373      2        255.23        106.75   \n",
       "2019-05-02 00:26:49  11477450      2        429.78        173.64   \n",
       "2019-05-02 00:27:49  11477488      2        402.06        167.55   \n",
       "2019-05-02 00:28:49  11477539      3        401.34        124.29   \n",
       "2019-05-02 00:29:49  11477605      3       1354.69        114.69   \n",
       "2019-05-02 00:30:49  11477677      3        415.81        127.65   \n",
       "...                       ...    ...           ...           ...   \n",
       "2019-05-02 23:30:51  11539365      7       1265.05         87.51   \n",
       "2019-05-02 23:31:51  11539471      3        385.11        113.29   \n",
       "2019-05-02 23:32:51  11539496      4        572.21        106.20   \n",
       "2019-05-02 23:33:51  11539585      5        757.15         91.95   \n",
       "2019-05-02 23:34:51  11539680     10       1924.06         86.33   \n",
       "2019-05-02 23:35:51  11539736      7       1044.32        112.03   \n",
       "2019-05-02 23:36:51  11539793      7       1255.16         89.74   \n",
       "2019-05-02 23:37:51  11539856     10       2143.21        125.08   \n",
       "2019-05-02 23:38:51  11539928      7       1230.97         71.90   \n",
       "2019-05-02 23:39:51  11540035      4        580.32        102.69   \n",
       "2019-05-02 23:40:51  11540090      6       1934.47         86.47   \n",
       "2019-05-02 23:41:51  11540132      4        510.76         88.82   \n",
       "2019-05-02 23:42:51  11540224      4        420.63         75.49   \n",
       "2019-05-02 23:43:51  11540307      3        609.28         75.26   \n",
       "2019-05-02 23:44:51  11540344      3        642.03        141.71   \n",
       "2019-05-02 23:45:51  11540408      8       1461.57         71.59   \n",
       "2019-05-02 23:46:51  11540485      4        997.30        105.76   \n",
       "2019-05-02 23:47:51  11540550      3        978.67        130.26   \n",
       "2019-05-02 23:48:51  11540604      4        572.21         79.91   \n",
       "2019-05-02 23:49:51  11540665      7       1885.74         84.52   \n",
       "2019-05-02 23:50:51  11540744      3        323.29        100.12   \n",
       "2019-05-02 23:51:51  11540811      3        296.45         75.38   \n",
       "2019-05-02 23:52:51  11540875      1        295.96        295.96   \n",
       "2019-05-02 23:53:51  11540913      6       1045.60         69.12   \n",
       "2019-05-02 23:54:51  11540995      4        755.00        153.64   \n",
       "2019-05-02 23:55:51  11541056      2        392.55         84.96   \n",
       "2019-05-02 23:56:51  11541082      5       1133.19        124.87   \n",
       "2019-05-02 23:57:51  11541153      1        886.99        886.99   \n",
       "2019-05-02 23:58:51  11541171      2        259.38        115.29   \n",
       "2019-05-02 23:59:51  11541226      6        823.94        110.75   \n",
       "\n",
       "                     res_time_max  res_time_avg  interval            create_at  \n",
       "create_at                                                                       \n",
       "2019-05-02 00:00:49        383.62         242.0        60  2019-05-02 00:00:49  \n",
       "2019-05-02 00:01:49        292.17         200.0        60  2019-05-02 00:01:49  \n",
       "2019-05-02 00:02:49        782.74         314.0        60  2019-05-02 00:02:49  \n",
       "2019-05-02 00:03:49        266.54         171.0        60  2019-05-02 00:03:49  \n",
       "2019-05-02 00:04:49        373.41         259.0        60  2019-05-02 00:04:49  \n",
       "2019-05-02 00:05:49        822.38         380.0        60  2019-05-02 00:05:49  \n",
       "2019-05-02 00:06:49        201.54         173.0        60  2019-05-02 00:06:49  \n",
       "2019-05-02 00:07:49        270.28         200.0        60  2019-05-02 00:07:49  \n",
       "2019-05-02 00:08:49         89.02          89.0        60  2019-05-02 00:08:49  \n",
       "2019-05-02 00:10:49        170.76         124.0        60  2019-05-02 00:10:49  \n",
       "2019-05-02 00:11:49        145.10         119.0        60  2019-05-02 00:11:49  \n",
       "2019-05-02 00:12:49        413.51         258.0        60  2019-05-02 00:12:49  \n",
       "2019-05-02 00:13:49        646.42         203.0        60  2019-05-02 00:13:49  \n",
       "2019-05-02 00:14:49        412.24         275.0        60  2019-05-02 00:14:49  \n",
       "2019-05-02 00:15:49        196.48         125.0        60  2019-05-02 00:15:49  \n",
       "2019-05-02 00:16:49        927.92         406.0        60  2019-05-02 00:16:49  \n",
       "2019-05-02 00:17:49        224.39         183.0        60  2019-05-02 00:17:49  \n",
       "2019-05-02 00:18:49        204.78         162.0        60  2019-05-02 00:18:49  \n",
       "2019-05-02 00:19:49        384.72         215.0        60  2019-05-02 00:19:49  \n",
       "2019-05-02 00:20:49        487.76         240.0        60  2019-05-02 00:20:49  \n",
       "2019-05-02 00:21:49        310.30         211.0        60  2019-05-02 00:21:49  \n",
       "2019-05-02 00:22:49        253.08         253.0        60  2019-05-02 00:22:49  \n",
       "2019-05-02 00:23:49        140.59         129.0        60  2019-05-02 00:23:49  \n",
       "2019-05-02 00:24:49        547.07         278.0        60  2019-05-02 00:24:49  \n",
       "2019-05-02 00:25:49        148.48         127.0        60  2019-05-02 00:25:49  \n",
       "2019-05-02 00:26:49        256.14         214.0        60  2019-05-02 00:26:49  \n",
       "2019-05-02 00:27:49        234.51         201.0        60  2019-05-02 00:27:49  \n",
       "2019-05-02 00:28:49        143.68         133.0        60  2019-05-02 00:28:49  \n",
       "2019-05-02 00:29:49        942.69         451.0        60  2019-05-02 00:29:49  \n",
       "2019-05-02 00:30:49        150.90         138.0        60  2019-05-02 00:30:49  \n",
       "...                           ...           ...       ...                  ...  \n",
       "2019-05-02 23:30:51        249.41         180.0        60  2019-05-02 23:30:51  \n",
       "2019-05-02 23:31:51        151.39         128.0        60  2019-05-02 23:31:51  \n",
       "2019-05-02 23:32:51        226.00         143.0        60  2019-05-02 23:32:51  \n",
       "2019-05-02 23:33:51        258.72         151.0        60  2019-05-02 23:33:51  \n",
       "2019-05-02 23:34:51        415.48         192.0        60  2019-05-02 23:34:51  \n",
       "2019-05-02 23:35:51        201.77         149.0        60  2019-05-02 23:35:51  \n",
       "2019-05-02 23:36:51        332.84         179.0        60  2019-05-02 23:36:51  \n",
       "2019-05-02 23:37:51        356.68         214.0        60  2019-05-02 23:37:51  \n",
       "2019-05-02 23:38:51        453.22         175.0        60  2019-05-02 23:38:51  \n",
       "2019-05-02 23:39:51        207.32         145.0        60  2019-05-02 23:39:51  \n",
       "2019-05-02 23:40:51       1033.02         322.0        60  2019-05-02 23:40:51  \n",
       "2019-05-02 23:41:51        185.52         127.0        60  2019-05-02 23:41:51  \n",
       "2019-05-02 23:42:51        137.94         105.0        60  2019-05-02 23:42:51  \n",
       "2019-05-02 23:43:51        288.71         203.0        60  2019-05-02 23:43:51  \n",
       "2019-05-02 23:44:51        283.59         214.0        60  2019-05-02 23:44:51  \n",
       "2019-05-02 23:45:51        645.84         182.0        60  2019-05-02 23:45:51  \n",
       "2019-05-02 23:46:51        455.57         249.0        60  2019-05-02 23:46:51  \n",
       "2019-05-02 23:47:51        649.39         326.0        60  2019-05-02 23:47:51  \n",
       "2019-05-02 23:48:51        213.63         143.0        60  2019-05-02 23:48:51  \n",
       "2019-05-02 23:49:51        654.95         269.0        60  2019-05-02 23:49:51  \n",
       "2019-05-02 23:50:51        118.35         107.0        60  2019-05-02 23:50:51  \n",
       "2019-05-02 23:51:51        139.71          98.0        60  2019-05-02 23:51:51  \n",
       "2019-05-02 23:52:51        295.96         295.0        60  2019-05-02 23:52:51  \n",
       "2019-05-02 23:53:51        446.44         174.0        60  2019-05-02 23:53:51  \n",
       "2019-05-02 23:54:51        238.43         188.0        60  2019-05-02 23:54:51  \n",
       "2019-05-02 23:55:51        307.59         196.0        60  2019-05-02 23:55:51  \n",
       "2019-05-02 23:56:51        318.95         226.0        60  2019-05-02 23:56:51  \n",
       "2019-05-02 23:57:51        886.99         886.0        60  2019-05-02 23:57:51  \n",
       "2019-05-02 23:58:51        144.09         129.0        60  2019-05-02 23:58:51  \n",
       "2019-05-02 23:59:51        226.56         137.0        60  2019-05-02 23:59:51  \n",
       "\n",
       "[865 rows x 8 columns]"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['2019-05-02']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    179496.0\n",
       "mean         60.0\n",
       "std           0.0\n",
       "min          60.0\n",
       "25%          60.0\n",
       "50%          60.0\n",
       "75%          60.0\n",
       "max          60.0\n",
       "Name: interval, dtype: float64"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.interval.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([60], dtype=int64)"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 这一列的所有不同值会生成一个列表 ，可见该列所有值都为60\n",
    "df.interval.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>create_at</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>create_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:07</th>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:01:07</th>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:02:07</th>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:03:07</th>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:04:07</th>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:05:07</th>\n",
       "      <td>5</td>\n",
       "      <td>521.28</td>\n",
       "      <td>80.64</td>\n",
       "      <td>126.17</td>\n",
       "      <td>104.0</td>\n",
       "      <td>2018-11-01 00:05:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:06:07</th>\n",
       "      <td>3</td>\n",
       "      <td>464.84</td>\n",
       "      <td>115.97</td>\n",
       "      <td>224.42</td>\n",
       "      <td>154.0</td>\n",
       "      <td>2018-11-01 00:06:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:07:07</th>\n",
       "      <td>2</td>\n",
       "      <td>337.58</td>\n",
       "      <td>75.58</td>\n",
       "      <td>262.00</td>\n",
       "      <td>168.0</td>\n",
       "      <td>2018-11-01 00:07:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:08:07</th>\n",
       "      <td>5</td>\n",
       "      <td>773.22</td>\n",
       "      <td>107.14</td>\n",
       "      <td>207.33</td>\n",
       "      <td>154.0</td>\n",
       "      <td>2018-11-01 00:08:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:09:07</th>\n",
       "      <td>4</td>\n",
       "      <td>669.66</td>\n",
       "      <td>140.26</td>\n",
       "      <td>225.21</td>\n",
       "      <td>167.0</td>\n",
       "      <td>2018-11-01 00:09:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:10:07</th>\n",
       "      <td>2</td>\n",
       "      <td>284.78</td>\n",
       "      <td>137.23</td>\n",
       "      <td>147.55</td>\n",
       "      <td>142.0</td>\n",
       "      <td>2018-11-01 00:10:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:11:07</th>\n",
       "      <td>2</td>\n",
       "      <td>281.44</td>\n",
       "      <td>107.79</td>\n",
       "      <td>173.65</td>\n",
       "      <td>140.0</td>\n",
       "      <td>2018-11-01 00:11:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:13:07</th>\n",
       "      <td>5</td>\n",
       "      <td>744.09</td>\n",
       "      <td>124.09</td>\n",
       "      <td>184.19</td>\n",
       "      <td>148.0</td>\n",
       "      <td>2018-11-01 00:13:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:14:07</th>\n",
       "      <td>3</td>\n",
       "      <td>502.55</td>\n",
       "      <td>124.74</td>\n",
       "      <td>228.34</td>\n",
       "      <td>167.0</td>\n",
       "      <td>2018-11-01 00:14:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:15:07</th>\n",
       "      <td>4</td>\n",
       "      <td>606.09</td>\n",
       "      <td>110.09</td>\n",
       "      <td>219.45</td>\n",
       "      <td>151.0</td>\n",
       "      <td>2018-11-01 00:15:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:16:07</th>\n",
       "      <td>1</td>\n",
       "      <td>189.27</td>\n",
       "      <td>189.27</td>\n",
       "      <td>189.27</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2018-11-01 00:16:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:17:07</th>\n",
       "      <td>2</td>\n",
       "      <td>259.86</td>\n",
       "      <td>109.71</td>\n",
       "      <td>150.15</td>\n",
       "      <td>129.0</td>\n",
       "      <td>2018-11-01 00:17:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:18:07</th>\n",
       "      <td>4</td>\n",
       "      <td>679.70</td>\n",
       "      <td>131.87</td>\n",
       "      <td>215.20</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2018-11-01 00:18:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:19:07</th>\n",
       "      <td>3</td>\n",
       "      <td>431.32</td>\n",
       "      <td>86.83</td>\n",
       "      <td>240.35</td>\n",
       "      <td>143.0</td>\n",
       "      <td>2018-11-01 00:19:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:20:07</th>\n",
       "      <td>2</td>\n",
       "      <td>312.04</td>\n",
       "      <td>143.40</td>\n",
       "      <td>168.64</td>\n",
       "      <td>156.0</td>\n",
       "      <td>2018-11-01 00:20:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:21:07</th>\n",
       "      <td>1</td>\n",
       "      <td>131.97</td>\n",
       "      <td>131.97</td>\n",
       "      <td>131.97</td>\n",
       "      <td>131.0</td>\n",
       "      <td>2018-11-01 00:21:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:22:07</th>\n",
       "      <td>3</td>\n",
       "      <td>587.98</td>\n",
       "      <td>133.60</td>\n",
       "      <td>299.42</td>\n",
       "      <td>195.0</td>\n",
       "      <td>2018-11-01 00:22:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:23:07</th>\n",
       "      <td>5</td>\n",
       "      <td>765.89</td>\n",
       "      <td>86.26</td>\n",
       "      <td>215.45</td>\n",
       "      <td>153.0</td>\n",
       "      <td>2018-11-01 00:23:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:24:07</th>\n",
       "      <td>2</td>\n",
       "      <td>339.82</td>\n",
       "      <td>117.91</td>\n",
       "      <td>221.91</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2018-11-01 00:24:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:25:07</th>\n",
       "      <td>4</td>\n",
       "      <td>515.23</td>\n",
       "      <td>120.84</td>\n",
       "      <td>142.90</td>\n",
       "      <td>128.0</td>\n",
       "      <td>2018-11-01 00:25:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:26:07</th>\n",
       "      <td>3</td>\n",
       "      <td>580.40</td>\n",
       "      <td>118.73</td>\n",
       "      <td>240.84</td>\n",
       "      <td>193.0</td>\n",
       "      <td>2018-11-01 00:26:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:27:07</th>\n",
       "      <td>3</td>\n",
       "      <td>471.82</td>\n",
       "      <td>104.81</td>\n",
       "      <td>200.54</td>\n",
       "      <td>157.0</td>\n",
       "      <td>2018-11-01 00:27:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:29:07</th>\n",
       "      <td>2</td>\n",
       "      <td>316.66</td>\n",
       "      <td>99.38</td>\n",
       "      <td>217.28</td>\n",
       "      <td>158.0</td>\n",
       "      <td>2018-11-01 00:29:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:30:07</th>\n",
       "      <td>4</td>\n",
       "      <td>786.39</td>\n",
       "      <td>142.04</td>\n",
       "      <td>233.86</td>\n",
       "      <td>196.0</td>\n",
       "      <td>2018-11-01 00:30:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:31:07</th>\n",
       "      <td>4</td>\n",
       "      <td>665.86</td>\n",
       "      <td>127.24</td>\n",
       "      <td>212.55</td>\n",
       "      <td>166.0</td>\n",
       "      <td>2018-11-01 00:31:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 22:41:21</th>\n",
       "      <td>11</td>\n",
       "      <td>1749.38</td>\n",
       "      <td>109.36</td>\n",
       "      <td>219.32</td>\n",
       "      <td>159.0</td>\n",
       "      <td>2019-05-30 22:41:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 22:42:21</th>\n",
       "      <td>14</td>\n",
       "      <td>3621.72</td>\n",
       "      <td>103.97</td>\n",
       "      <td>1194.12</td>\n",
       "      <td>258.0</td>\n",
       "      <td>2019-05-30 22:42:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 22:43:21</th>\n",
       "      <td>8</td>\n",
       "      <td>1692.43</td>\n",
       "      <td>88.15</td>\n",
       "      <td>493.56</td>\n",
       "      <td>211.0</td>\n",
       "      <td>2019-05-30 22:43:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 22:44:21</th>\n",
       "      <td>8</td>\n",
       "      <td>2496.29</td>\n",
       "      <td>98.70</td>\n",
       "      <td>946.82</td>\n",
       "      <td>312.0</td>\n",
       "      <td>2019-05-30 22:44:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 22:45:21</th>\n",
       "      <td>4</td>\n",
       "      <td>1004.29</td>\n",
       "      <td>159.22</td>\n",
       "      <td>342.42</td>\n",
       "      <td>251.0</td>\n",
       "      <td>2019-05-30 22:45:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 22:46:21</th>\n",
       "      <td>8</td>\n",
       "      <td>1127.33</td>\n",
       "      <td>99.60</td>\n",
       "      <td>252.70</td>\n",
       "      <td>140.0</td>\n",
       "      <td>2019-05-30 22:46:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 22:47:21</th>\n",
       "      <td>11</td>\n",
       "      <td>2621.67</td>\n",
       "      <td>103.73</td>\n",
       "      <td>532.72</td>\n",
       "      <td>238.0</td>\n",
       "      <td>2019-05-30 22:47:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 22:48:21</th>\n",
       "      <td>8</td>\n",
       "      <td>1672.94</td>\n",
       "      <td>118.61</td>\n",
       "      <td>328.94</td>\n",
       "      <td>209.0</td>\n",
       "      <td>2019-05-30 22:48:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 22:49:21</th>\n",
       "      <td>9</td>\n",
       "      <td>1766.83</td>\n",
       "      <td>86.40</td>\n",
       "      <td>290.11</td>\n",
       "      <td>196.0</td>\n",
       "      <td>2019-05-30 22:49:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 22:50:21</th>\n",
       "      <td>13</td>\n",
       "      <td>3354.25</td>\n",
       "      <td>109.35</td>\n",
       "      <td>870.09</td>\n",
       "      <td>258.0</td>\n",
       "      <td>2019-05-30 22:50:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 22:51:21</th>\n",
       "      <td>6</td>\n",
       "      <td>1508.23</td>\n",
       "      <td>151.89</td>\n",
       "      <td>528.19</td>\n",
       "      <td>251.0</td>\n",
       "      <td>2019-05-30 22:51:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 22:52:21</th>\n",
       "      <td>11</td>\n",
       "      <td>1702.00</td>\n",
       "      <td>88.86</td>\n",
       "      <td>232.60</td>\n",
       "      <td>154.0</td>\n",
       "      <td>2019-05-30 22:52:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 22:53:21</th>\n",
       "      <td>13</td>\n",
       "      <td>2463.45</td>\n",
       "      <td>72.93</td>\n",
       "      <td>331.06</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2019-05-30 22:53:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 22:54:21</th>\n",
       "      <td>6</td>\n",
       "      <td>1525.68</td>\n",
       "      <td>123.02</td>\n",
       "      <td>476.26</td>\n",
       "      <td>254.0</td>\n",
       "      <td>2019-05-30 22:54:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 22:55:21</th>\n",
       "      <td>13</td>\n",
       "      <td>2556.67</td>\n",
       "      <td>85.51</td>\n",
       "      <td>383.73</td>\n",
       "      <td>196.0</td>\n",
       "      <td>2019-05-30 22:55:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 22:56:21</th>\n",
       "      <td>6</td>\n",
       "      <td>1784.40</td>\n",
       "      <td>229.68</td>\n",
       "      <td>476.04</td>\n",
       "      <td>297.0</td>\n",
       "      <td>2019-05-30 22:56:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 22:57:21</th>\n",
       "      <td>6</td>\n",
       "      <td>3338.22</td>\n",
       "      <td>110.07</td>\n",
       "      <td>1818.86</td>\n",
       "      <td>556.0</td>\n",
       "      <td>2019-05-30 22:57:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 22:58:21</th>\n",
       "      <td>15</td>\n",
       "      <td>4589.76</td>\n",
       "      <td>129.28</td>\n",
       "      <td>1051.45</td>\n",
       "      <td>305.0</td>\n",
       "      <td>2019-05-30 22:58:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 22:59:21</th>\n",
       "      <td>6</td>\n",
       "      <td>1068.02</td>\n",
       "      <td>111.77</td>\n",
       "      <td>389.24</td>\n",
       "      <td>178.0</td>\n",
       "      <td>2019-05-30 22:59:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 23:00:21</th>\n",
       "      <td>5</td>\n",
       "      <td>579.21</td>\n",
       "      <td>73.64</td>\n",
       "      <td>155.20</td>\n",
       "      <td>115.0</td>\n",
       "      <td>2019-05-30 23:00:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 23:01:21</th>\n",
       "      <td>11</td>\n",
       "      <td>3005.36</td>\n",
       "      <td>109.35</td>\n",
       "      <td>543.06</td>\n",
       "      <td>273.0</td>\n",
       "      <td>2019-05-30 23:01:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 23:02:21</th>\n",
       "      <td>8</td>\n",
       "      <td>1324.52</td>\n",
       "      <td>63.97</td>\n",
       "      <td>335.66</td>\n",
       "      <td>165.0</td>\n",
       "      <td>2019-05-30 23:02:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 23:03:21</th>\n",
       "      <td>8</td>\n",
       "      <td>2568.12</td>\n",
       "      <td>79.89</td>\n",
       "      <td>1027.96</td>\n",
       "      <td>321.0</td>\n",
       "      <td>2019-05-30 23:03:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 23:04:21</th>\n",
       "      <td>10</td>\n",
       "      <td>2903.91</td>\n",
       "      <td>125.55</td>\n",
       "      <td>883.17</td>\n",
       "      <td>290.0</td>\n",
       "      <td>2019-05-30 23:04:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 23:05:21</th>\n",
       "      <td>10</td>\n",
       "      <td>2533.43</td>\n",
       "      <td>155.12</td>\n",
       "      <td>359.90</td>\n",
       "      <td>253.0</td>\n",
       "      <td>2019-05-30 23:05:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 23:06:21</th>\n",
       "      <td>11</td>\n",
       "      <td>2783.48</td>\n",
       "      <td>99.24</td>\n",
       "      <td>489.90</td>\n",
       "      <td>253.0</td>\n",
       "      <td>2019-05-30 23:06:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 23:07:21</th>\n",
       "      <td>10</td>\n",
       "      <td>1951.10</td>\n",
       "      <td>85.37</td>\n",
       "      <td>529.51</td>\n",
       "      <td>195.0</td>\n",
       "      <td>2019-05-30 23:07:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 23:08:21</th>\n",
       "      <td>3</td>\n",
       "      <td>494.17</td>\n",
       "      <td>103.95</td>\n",
       "      <td>211.47</td>\n",
       "      <td>164.0</td>\n",
       "      <td>2019-05-30 23:08:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 23:09:21</th>\n",
       "      <td>9</td>\n",
       "      <td>1798.28</td>\n",
       "      <td>101.11</td>\n",
       "      <td>433.30</td>\n",
       "      <td>199.0</td>\n",
       "      <td>2019-05-30 23:09:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 23:10:21</th>\n",
       "      <td>6</td>\n",
       "      <td>1017.97</td>\n",
       "      <td>74.45</td>\n",
       "      <td>298.97</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2019-05-30 23:10:21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>179496 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "create_at                                                              \n",
       "2018-11-01 00:00:07      8       1057.31         88.75        177.72   \n",
       "2018-11-01 00:01:07      5        749.12        103.79        240.38   \n",
       "2018-11-01 00:02:07      5        845.84        136.31        225.73   \n",
       "2018-11-01 00:03:07      9       1305.52         90.12        196.61   \n",
       "2018-11-01 00:04:07      3        568.89        138.45        232.02   \n",
       "2018-11-01 00:05:07      5        521.28         80.64        126.17   \n",
       "2018-11-01 00:06:07      3        464.84        115.97        224.42   \n",
       "2018-11-01 00:07:07      2        337.58         75.58        262.00   \n",
       "2018-11-01 00:08:07      5        773.22        107.14        207.33   \n",
       "2018-11-01 00:09:07      4        669.66        140.26        225.21   \n",
       "2018-11-01 00:10:07      2        284.78        137.23        147.55   \n",
       "2018-11-01 00:11:07      2        281.44        107.79        173.65   \n",
       "2018-11-01 00:13:07      5        744.09        124.09        184.19   \n",
       "2018-11-01 00:14:07      3        502.55        124.74        228.34   \n",
       "2018-11-01 00:15:07      4        606.09        110.09        219.45   \n",
       "2018-11-01 00:16:07      1        189.27        189.27        189.27   \n",
       "2018-11-01 00:17:07      2        259.86        109.71        150.15   \n",
       "2018-11-01 00:18:07      4        679.70        131.87        215.20   \n",
       "2018-11-01 00:19:07      3        431.32         86.83        240.35   \n",
       "2018-11-01 00:20:07      2        312.04        143.40        168.64   \n",
       "2018-11-01 00:21:07      1        131.97        131.97        131.97   \n",
       "2018-11-01 00:22:07      3        587.98        133.60        299.42   \n",
       "2018-11-01 00:23:07      5        765.89         86.26        215.45   \n",
       "2018-11-01 00:24:07      2        339.82        117.91        221.91   \n",
       "2018-11-01 00:25:07      4        515.23        120.84        142.90   \n",
       "2018-11-01 00:26:07      3        580.40        118.73        240.84   \n",
       "2018-11-01 00:27:07      3        471.82        104.81        200.54   \n",
       "2018-11-01 00:29:07      2        316.66         99.38        217.28   \n",
       "2018-11-01 00:30:07      4        786.39        142.04        233.86   \n",
       "2018-11-01 00:31:07      4        665.86        127.24        212.55   \n",
       "...                    ...           ...           ...           ...   \n",
       "2019-05-30 22:41:21     11       1749.38        109.36        219.32   \n",
       "2019-05-30 22:42:21     14       3621.72        103.97       1194.12   \n",
       "2019-05-30 22:43:21      8       1692.43         88.15        493.56   \n",
       "2019-05-30 22:44:21      8       2496.29         98.70        946.82   \n",
       "2019-05-30 22:45:21      4       1004.29        159.22        342.42   \n",
       "2019-05-30 22:46:21      8       1127.33         99.60        252.70   \n",
       "2019-05-30 22:47:21     11       2621.67        103.73        532.72   \n",
       "2019-05-30 22:48:21      8       1672.94        118.61        328.94   \n",
       "2019-05-30 22:49:21      9       1766.83         86.40        290.11   \n",
       "2019-05-30 22:50:21     13       3354.25        109.35        870.09   \n",
       "2019-05-30 22:51:21      6       1508.23        151.89        528.19   \n",
       "2019-05-30 22:52:21     11       1702.00         88.86        232.60   \n",
       "2019-05-30 22:53:21     13       2463.45         72.93        331.06   \n",
       "2019-05-30 22:54:21      6       1525.68        123.02        476.26   \n",
       "2019-05-30 22:55:21     13       2556.67         85.51        383.73   \n",
       "2019-05-30 22:56:21      6       1784.40        229.68        476.04   \n",
       "2019-05-30 22:57:21      6       3338.22        110.07       1818.86   \n",
       "2019-05-30 22:58:21     15       4589.76        129.28       1051.45   \n",
       "2019-05-30 22:59:21      6       1068.02        111.77        389.24   \n",
       "2019-05-30 23:00:21      5        579.21         73.64        155.20   \n",
       "2019-05-30 23:01:21     11       3005.36        109.35        543.06   \n",
       "2019-05-30 23:02:21      8       1324.52         63.97        335.66   \n",
       "2019-05-30 23:03:21      8       2568.12         79.89       1027.96   \n",
       "2019-05-30 23:04:21     10       2903.91        125.55        883.17   \n",
       "2019-05-30 23:05:21     10       2533.43        155.12        359.90   \n",
       "2019-05-30 23:06:21     11       2783.48         99.24        489.90   \n",
       "2019-05-30 23:07:21     10       1951.10         85.37        529.51   \n",
       "2019-05-30 23:08:21      3        494.17        103.95        211.47   \n",
       "2019-05-30 23:09:21      9       1798.28        101.11        433.30   \n",
       "2019-05-30 23:10:21      6       1017.97         74.45        298.97   \n",
       "\n",
       "                     res_time_avg            create_at  \n",
       "create_at                                               \n",
       "2018-11-01 00:00:07         132.0  2018-11-01 00:00:07  \n",
       "2018-11-01 00:01:07         149.0  2018-11-01 00:01:07  \n",
       "2018-11-01 00:02:07         169.0  2018-11-01 00:02:07  \n",
       "2018-11-01 00:03:07         145.0  2018-11-01 00:03:07  \n",
       "2018-11-01 00:04:07         189.0  2018-11-01 00:04:07  \n",
       "2018-11-01 00:05:07         104.0  2018-11-01 00:05:07  \n",
       "2018-11-01 00:06:07         154.0  2018-11-01 00:06:07  \n",
       "2018-11-01 00:07:07         168.0  2018-11-01 00:07:07  \n",
       "2018-11-01 00:08:07         154.0  2018-11-01 00:08:07  \n",
       "2018-11-01 00:09:07         167.0  2018-11-01 00:09:07  \n",
       "2018-11-01 00:10:07         142.0  2018-11-01 00:10:07  \n",
       "2018-11-01 00:11:07         140.0  2018-11-01 00:11:07  \n",
       "2018-11-01 00:13:07         148.0  2018-11-01 00:13:07  \n",
       "2018-11-01 00:14:07         167.0  2018-11-01 00:14:07  \n",
       "2018-11-01 00:15:07         151.0  2018-11-01 00:15:07  \n",
       "2018-11-01 00:16:07         189.0  2018-11-01 00:16:07  \n",
       "2018-11-01 00:17:07         129.0  2018-11-01 00:17:07  \n",
       "2018-11-01 00:18:07         169.0  2018-11-01 00:18:07  \n",
       "2018-11-01 00:19:07         143.0  2018-11-01 00:19:07  \n",
       "2018-11-01 00:20:07         156.0  2018-11-01 00:20:07  \n",
       "2018-11-01 00:21:07         131.0  2018-11-01 00:21:07  \n",
       "2018-11-01 00:22:07         195.0  2018-11-01 00:22:07  \n",
       "2018-11-01 00:23:07         153.0  2018-11-01 00:23:07  \n",
       "2018-11-01 00:24:07         169.0  2018-11-01 00:24:07  \n",
       "2018-11-01 00:25:07         128.0  2018-11-01 00:25:07  \n",
       "2018-11-01 00:26:07         193.0  2018-11-01 00:26:07  \n",
       "2018-11-01 00:27:07         157.0  2018-11-01 00:27:07  \n",
       "2018-11-01 00:29:07         158.0  2018-11-01 00:29:07  \n",
       "2018-11-01 00:30:07         196.0  2018-11-01 00:30:07  \n",
       "2018-11-01 00:31:07         166.0  2018-11-01 00:31:07  \n",
       "...                           ...                  ...  \n",
       "2019-05-30 22:41:21         159.0  2019-05-30 22:41:21  \n",
       "2019-05-30 22:42:21         258.0  2019-05-30 22:42:21  \n",
       "2019-05-30 22:43:21         211.0  2019-05-30 22:43:21  \n",
       "2019-05-30 22:44:21         312.0  2019-05-30 22:44:21  \n",
       "2019-05-30 22:45:21         251.0  2019-05-30 22:45:21  \n",
       "2019-05-30 22:46:21         140.0  2019-05-30 22:46:21  \n",
       "2019-05-30 22:47:21         238.0  2019-05-30 22:47:21  \n",
       "2019-05-30 22:48:21         209.0  2019-05-30 22:48:21  \n",
       "2019-05-30 22:49:21         196.0  2019-05-30 22:49:21  \n",
       "2019-05-30 22:50:21         258.0  2019-05-30 22:50:21  \n",
       "2019-05-30 22:51:21         251.0  2019-05-30 22:51:21  \n",
       "2019-05-30 22:52:21         154.0  2019-05-30 22:52:21  \n",
       "2019-05-30 22:53:21         189.0  2019-05-30 22:53:21  \n",
       "2019-05-30 22:54:21         254.0  2019-05-30 22:54:21  \n",
       "2019-05-30 22:55:21         196.0  2019-05-30 22:55:21  \n",
       "2019-05-30 22:56:21         297.0  2019-05-30 22:56:21  \n",
       "2019-05-30 22:57:21         556.0  2019-05-30 22:57:21  \n",
       "2019-05-30 22:58:21         305.0  2019-05-30 22:58:21  \n",
       "2019-05-30 22:59:21         178.0  2019-05-30 22:59:21  \n",
       "2019-05-30 23:00:21         115.0  2019-05-30 23:00:21  \n",
       "2019-05-30 23:01:21         273.0  2019-05-30 23:01:21  \n",
       "2019-05-30 23:02:21         165.0  2019-05-30 23:02:21  \n",
       "2019-05-30 23:03:21         321.0  2019-05-30 23:03:21  \n",
       "2019-05-30 23:04:21         290.0  2019-05-30 23:04:21  \n",
       "2019-05-30 23:05:21         253.0  2019-05-30 23:05:21  \n",
       "2019-05-30 23:06:21         253.0  2019-05-30 23:06:21  \n",
       "2019-05-30 23:07:21         195.0  2019-05-30 23:07:21  \n",
       "2019-05-30 23:08:21         164.0  2019-05-30 23:08:21  \n",
       "2019-05-30 23:09:21         199.0  2019-05-30 23:09:21  \n",
       "2019-05-30 23:10:21         169.0  2019-05-30 23:10:21  \n",
       "\n",
       "[179496 rows x 6 columns]"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 删除id和interval这两列\n",
    "df = df.drop(['id','interval'], axis = 1 )\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 179496 entries, 2018-11-01 00:00:07 to 2019-05-30 23:10:21\n",
      "Data columns (total 6 columns):\n",
      "count           179496 non-null int64\n",
      "res_time_sum    179496 non-null float64\n",
      "res_time_min    179496 non-null float64\n",
      "res_time_max    179496 non-null float64\n",
      "res_time_avg    179496 non-null float64\n",
      "create_at       179496 non-null object\n",
      "dtypes: float64(4), int64(1), object(1)\n",
      "memory usage: 9.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.175909</td>\n",
       "      <td>1393.177832</td>\n",
       "      <td>108.419626</td>\n",
       "      <td>359.880374</td>\n",
       "      <td>187.812208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.325160</td>\n",
       "      <td>1499.486073</td>\n",
       "      <td>79.640693</td>\n",
       "      <td>638.919827</td>\n",
       "      <td>224.464813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>36.550000</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>36.550000</td>\n",
       "      <td>36.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>607.707500</td>\n",
       "      <td>83.410000</td>\n",
       "      <td>198.280000</td>\n",
       "      <td>144.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>1154.905000</td>\n",
       "      <td>97.120000</td>\n",
       "      <td>256.090000</td>\n",
       "      <td>167.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>1834.117500</td>\n",
       "      <td>116.990000</td>\n",
       "      <td>374.410000</td>\n",
       "      <td>202.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>142650.550000</td>\n",
       "      <td>18896.640000</td>\n",
       "      <td>142468.270000</td>\n",
       "      <td>71325.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count   res_time_sum   res_time_min   res_time_max  \\\n",
       "count  179496.000000  179496.000000  179496.000000  179496.000000   \n",
       "mean        7.175909    1393.177832     108.419626     359.880374   \n",
       "std         4.325160    1499.486073      79.640693     638.919827   \n",
       "min         1.000000      36.550000       3.210000      36.550000   \n",
       "25%         4.000000     607.707500      83.410000     198.280000   \n",
       "50%         7.000000    1154.905000      97.120000     256.090000   \n",
       "75%        10.000000    1834.117500     116.990000     374.410000   \n",
       "max        31.000000  142650.550000   18896.640000  142468.270000   \n",
       "\n",
       "        res_time_avg  \n",
       "count  179496.000000  \n",
       "mean      187.812208  \n",
       "std       224.464813  \n",
       "min        36.000000  \n",
       "25%       144.000000  \n",
       "50%       167.000000  \n",
       "75%       202.000000  \n",
       "max     71325.000000  "
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['count'].hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['count'].hist(bins = 30) # 30个柱子\n",
    "plt.show()\n",
    "# 由此可看出接口调用分布情况，大部分1分钟10次以内"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 以某一天的数据为基准 绘制一天时段的接口调用情况\n",
    "df['2019-05-01']['count'].plot()\n",
    "plt.show()\n",
    "# 可以看出凌晨调用极少 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>create_at</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-05-01 00:00:00</th>\n",
       "      <td>4.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 01:00:00</th>\n",
       "      <td>2.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 02:00:00</th>\n",
       "      <td>1.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 03:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 04:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 05:00:00</th>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 06:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 07:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 08:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 09:00:00</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 10:00:00</th>\n",
       "      <td>1.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 11:00:00</th>\n",
       "      <td>1.604651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 12:00:00</th>\n",
       "      <td>3.298246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 13:00:00</th>\n",
       "      <td>6.866667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 14:00:00</th>\n",
       "      <td>10.483333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 15:00:00</th>\n",
       "      <td>12.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 16:00:00</th>\n",
       "      <td>9.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 17:00:00</th>\n",
       "      <td>7.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 18:00:00</th>\n",
       "      <td>6.783333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 19:00:00</th>\n",
       "      <td>9.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 20:00:00</th>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 21:00:00</th>\n",
       "      <td>10.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 22:00:00</th>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 23:00:00</th>\n",
       "      <td>5.083333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         count\n",
       "create_at                     \n",
       "2019-05-01 00:00:00   4.428571\n",
       "2019-05-01 01:00:00   2.272727\n",
       "2019-05-01 02:00:00   1.833333\n",
       "2019-05-01 03:00:00        NaN\n",
       "2019-05-01 04:00:00        NaN\n",
       "2019-05-01 05:00:00   2.000000\n",
       "2019-05-01 06:00:00        NaN\n",
       "2019-05-01 07:00:00        NaN\n",
       "2019-05-01 08:00:00        NaN\n",
       "2019-05-01 09:00:00   1.000000\n",
       "2019-05-01 10:00:00   1.400000\n",
       "2019-05-01 11:00:00   1.604651\n",
       "2019-05-01 12:00:00   3.298246\n",
       "2019-05-01 13:00:00   6.866667\n",
       "2019-05-01 14:00:00  10.483333\n",
       "2019-05-01 15:00:00  12.333333\n",
       "2019-05-01 16:00:00   9.916667\n",
       "2019-05-01 17:00:00   7.666667\n",
       "2019-05-01 18:00:00   6.783333\n",
       "2019-05-01 19:00:00   9.850000\n",
       "2019-05-01 20:00:00  11.000000\n",
       "2019-05-01 21:00:00  10.416667\n",
       "2019-05-01 22:00:00   8.000000\n",
       "2019-05-01 23:00:00   5.083333"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df['2019-05-01'][['count']]\n",
    "df2 = df2[['count']].resample('1H').mean() # 按小时重新采样\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2['count'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 折线图和直方图可以看到业务的高峰时段在什么地方，看不出具体时间，绘制柱状图\n",
    "plt.figure(figsize = (10,3)) # 设置绘图大小，单位：英寸\n",
    "df2['count'].plot(kind='bar') # 这是调用pandas里集成的绘图，实际底层也是调用matplotlib\n",
    "plt.xticks(rotation = 60) # 文字倾斜60度\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aeCAi/gx4FfjTxuYHgcci4gec+4fgdAtdzAF/GxEnU0rl7v8FUnu8bFGSMuGUiyRlwkCXpEwY6JKUCQNdkjJhoEtSJgx0ScqEgS5JmTDQJSkT/we27EYkkQxuhgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 分析有无异常时段，接口访问过于频繁，可能受黑客潮水攻击\n",
    "df['2019-05-01'][['count']].boxplot(showmeans = True, meanline = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>create_at</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>create_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 20:47:09</th>\n",
       "      <td>21</td>\n",
       "      <td>3117.20</td>\n",
       "      <td>84.90</td>\n",
       "      <td>260.82</td>\n",
       "      <td>148.0</td>\n",
       "      <td>2018-11-01 20:47:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 21:03:09</th>\n",
       "      <td>21</td>\n",
       "      <td>3706.20</td>\n",
       "      <td>78.12</td>\n",
       "      <td>321.47</td>\n",
       "      <td>176.0</td>\n",
       "      <td>2018-11-01 21:03:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 21:13:09</th>\n",
       "      <td>24</td>\n",
       "      <td>4602.03</td>\n",
       "      <td>76.31</td>\n",
       "      <td>391.12</td>\n",
       "      <td>191.0</td>\n",
       "      <td>2018-11-01 21:13:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-02 21:34:11</th>\n",
       "      <td>30</td>\n",
       "      <td>4610.15</td>\n",
       "      <td>72.49</td>\n",
       "      <td>463.41</td>\n",
       "      <td>153.0</td>\n",
       "      <td>2018-11-02 21:34:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-03 14:20:13</th>\n",
       "      <td>21</td>\n",
       "      <td>3113.93</td>\n",
       "      <td>74.29</td>\n",
       "      <td>266.20</td>\n",
       "      <td>148.0</td>\n",
       "      <td>2018-11-03 14:20:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-03 20:16:13</th>\n",
       "      <td>21</td>\n",
       "      <td>2992.24</td>\n",
       "      <td>86.28</td>\n",
       "      <td>246.71</td>\n",
       "      <td>142.0</td>\n",
       "      <td>2018-11-03 20:16:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-03 22:01:13</th>\n",
       "      <td>22</td>\n",
       "      <td>3615.11</td>\n",
       "      <td>108.00</td>\n",
       "      <td>231.49</td>\n",
       "      <td>164.0</td>\n",
       "      <td>2018-11-03 22:01:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-03 22:42:13</th>\n",
       "      <td>28</td>\n",
       "      <td>4332.65</td>\n",
       "      <td>76.26</td>\n",
       "      <td>263.33</td>\n",
       "      <td>154.0</td>\n",
       "      <td>2018-11-03 22:42:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-05 15:49:17</th>\n",
       "      <td>24</td>\n",
       "      <td>3723.64</td>\n",
       "      <td>88.97</td>\n",
       "      <td>280.92</td>\n",
       "      <td>155.0</td>\n",
       "      <td>2018-11-05 15:49:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-05 19:33:17</th>\n",
       "      <td>21</td>\n",
       "      <td>2831.71</td>\n",
       "      <td>78.66</td>\n",
       "      <td>170.69</td>\n",
       "      <td>134.0</td>\n",
       "      <td>2018-11-05 19:33:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-06 20:49:20</th>\n",
       "      <td>21</td>\n",
       "      <td>3414.39</td>\n",
       "      <td>87.02</td>\n",
       "      <td>257.39</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2018-11-06 20:49:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-08 15:56:23</th>\n",
       "      <td>21</td>\n",
       "      <td>3356.42</td>\n",
       "      <td>85.43</td>\n",
       "      <td>252.38</td>\n",
       "      <td>159.0</td>\n",
       "      <td>2018-11-08 15:56:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-08 20:50:23</th>\n",
       "      <td>23</td>\n",
       "      <td>3998.72</td>\n",
       "      <td>90.64</td>\n",
       "      <td>398.60</td>\n",
       "      <td>173.0</td>\n",
       "      <td>2018-11-08 20:50:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-08 20:51:23</th>\n",
       "      <td>21</td>\n",
       "      <td>3736.10</td>\n",
       "      <td>87.71</td>\n",
       "      <td>327.77</td>\n",
       "      <td>177.0</td>\n",
       "      <td>2018-11-08 20:51:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-08 20:59:23</th>\n",
       "      <td>21</td>\n",
       "      <td>3161.50</td>\n",
       "      <td>89.86</td>\n",
       "      <td>423.33</td>\n",
       "      <td>150.0</td>\n",
       "      <td>2018-11-08 20:59:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-09 20:49:25</th>\n",
       "      <td>21</td>\n",
       "      <td>3962.84</td>\n",
       "      <td>129.44</td>\n",
       "      <td>322.40</td>\n",
       "      <td>188.0</td>\n",
       "      <td>2018-11-09 20:49:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-09 21:41:25</th>\n",
       "      <td>21</td>\n",
       "      <td>3199.91</td>\n",
       "      <td>75.82</td>\n",
       "      <td>276.96</td>\n",
       "      <td>152.0</td>\n",
       "      <td>2018-11-09 21:41:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-09 22:09:25</th>\n",
       "      <td>22</td>\n",
       "      <td>3582.53</td>\n",
       "      <td>108.02</td>\n",
       "      <td>246.32</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2018-11-09 22:09:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-10 20:07:26</th>\n",
       "      <td>22</td>\n",
       "      <td>3362.64</td>\n",
       "      <td>80.28</td>\n",
       "      <td>225.21</td>\n",
       "      <td>152.0</td>\n",
       "      <td>2018-11-10 20:07:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-10 21:17:26</th>\n",
       "      <td>21</td>\n",
       "      <td>3407.67</td>\n",
       "      <td>100.55</td>\n",
       "      <td>263.82</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2018-11-10 21:17:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-10 21:48:26</th>\n",
       "      <td>21</td>\n",
       "      <td>3274.11</td>\n",
       "      <td>84.12</td>\n",
       "      <td>354.66</td>\n",
       "      <td>155.0</td>\n",
       "      <td>2018-11-10 21:48:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-10 22:03:26</th>\n",
       "      <td>21</td>\n",
       "      <td>3525.31</td>\n",
       "      <td>119.81</td>\n",
       "      <td>283.33</td>\n",
       "      <td>167.0</td>\n",
       "      <td>2018-11-10 22:03:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-11 17:02:28</th>\n",
       "      <td>21</td>\n",
       "      <td>3123.46</td>\n",
       "      <td>68.51</td>\n",
       "      <td>359.94</td>\n",
       "      <td>148.0</td>\n",
       "      <td>2018-11-11 17:02:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-11 20:45:28</th>\n",
       "      <td>21</td>\n",
       "      <td>3515.21</td>\n",
       "      <td>85.81</td>\n",
       "      <td>297.33</td>\n",
       "      <td>167.0</td>\n",
       "      <td>2018-11-11 20:45:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-11 20:48:28</th>\n",
       "      <td>21</td>\n",
       "      <td>3006.97</td>\n",
       "      <td>83.48</td>\n",
       "      <td>353.50</td>\n",
       "      <td>143.0</td>\n",
       "      <td>2018-11-11 20:48:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-11 22:17:28</th>\n",
       "      <td>23</td>\n",
       "      <td>3709.56</td>\n",
       "      <td>92.62</td>\n",
       "      <td>314.90</td>\n",
       "      <td>161.0</td>\n",
       "      <td>2018-11-11 22:17:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-12 16:28:30</th>\n",
       "      <td>22</td>\n",
       "      <td>3328.76</td>\n",
       "      <td>78.25</td>\n",
       "      <td>257.35</td>\n",
       "      <td>151.0</td>\n",
       "      <td>2018-11-12 16:28:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-12 21:01:30</th>\n",
       "      <td>21</td>\n",
       "      <td>3177.52</td>\n",
       "      <td>92.07</td>\n",
       "      <td>226.59</td>\n",
       "      <td>151.0</td>\n",
       "      <td>2018-11-12 21:01:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-12 21:06:30</th>\n",
       "      <td>21</td>\n",
       "      <td>3887.31</td>\n",
       "      <td>100.05</td>\n",
       "      <td>292.41</td>\n",
       "      <td>185.0</td>\n",
       "      <td>2018-11-12 21:06:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-13 15:51:32</th>\n",
       "      <td>23</td>\n",
       "      <td>3505.80</td>\n",
       "      <td>78.76</td>\n",
       "      <td>249.86</td>\n",
       "      <td>152.0</td>\n",
       "      <td>2018-11-13 15:51:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-27 16:08:18</th>\n",
       "      <td>27</td>\n",
       "      <td>13177.00</td>\n",
       "      <td>80.89</td>\n",
       "      <td>2768.33</td>\n",
       "      <td>488.0</td>\n",
       "      <td>2019-05-27 16:08:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-27 18:29:18</th>\n",
       "      <td>23</td>\n",
       "      <td>5264.64</td>\n",
       "      <td>90.01</td>\n",
       "      <td>515.05</td>\n",
       "      <td>228.0</td>\n",
       "      <td>2019-05-27 18:29:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-27 19:28:18</th>\n",
       "      <td>21</td>\n",
       "      <td>4612.10</td>\n",
       "      <td>93.98</td>\n",
       "      <td>372.50</td>\n",
       "      <td>219.0</td>\n",
       "      <td>2019-05-27 19:28:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-27 19:49:18</th>\n",
       "      <td>28</td>\n",
       "      <td>5647.21</td>\n",
       "      <td>78.28</td>\n",
       "      <td>648.65</td>\n",
       "      <td>201.0</td>\n",
       "      <td>2019-05-27 19:49:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-27 20:03:18</th>\n",
       "      <td>21</td>\n",
       "      <td>5146.42</td>\n",
       "      <td>97.18</td>\n",
       "      <td>1250.87</td>\n",
       "      <td>245.0</td>\n",
       "      <td>2019-05-27 20:03:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-27 20:05:18</th>\n",
       "      <td>21</td>\n",
       "      <td>5242.64</td>\n",
       "      <td>113.51</td>\n",
       "      <td>507.65</td>\n",
       "      <td>249.0</td>\n",
       "      <td>2019-05-27 20:05:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-27 21:13:18</th>\n",
       "      <td>26</td>\n",
       "      <td>4656.33</td>\n",
       "      <td>102.24</td>\n",
       "      <td>300.69</td>\n",
       "      <td>179.0</td>\n",
       "      <td>2019-05-27 21:13:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-27 21:16:18</th>\n",
       "      <td>24</td>\n",
       "      <td>5160.23</td>\n",
       "      <td>95.19</td>\n",
       "      <td>538.70</td>\n",
       "      <td>215.0</td>\n",
       "      <td>2019-05-27 21:16:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-27 21:58:18</th>\n",
       "      <td>25</td>\n",
       "      <td>9587.37</td>\n",
       "      <td>97.71</td>\n",
       "      <td>1304.84</td>\n",
       "      <td>383.0</td>\n",
       "      <td>2019-05-27 21:58:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-27 22:01:18</th>\n",
       "      <td>21</td>\n",
       "      <td>5813.94</td>\n",
       "      <td>118.05</td>\n",
       "      <td>1130.25</td>\n",
       "      <td>276.0</td>\n",
       "      <td>2019-05-27 22:01:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-28 16:19:19</th>\n",
       "      <td>24</td>\n",
       "      <td>5168.07</td>\n",
       "      <td>94.52</td>\n",
       "      <td>869.76</td>\n",
       "      <td>215.0</td>\n",
       "      <td>2019-05-28 16:19:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-28 20:51:19</th>\n",
       "      <td>23</td>\n",
       "      <td>7090.56</td>\n",
       "      <td>89.50</td>\n",
       "      <td>1613.17</td>\n",
       "      <td>308.0</td>\n",
       "      <td>2019-05-28 20:51:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-28 20:52:19</th>\n",
       "      <td>23</td>\n",
       "      <td>5801.02</td>\n",
       "      <td>77.39</td>\n",
       "      <td>802.72</td>\n",
       "      <td>252.0</td>\n",
       "      <td>2019-05-28 20:52:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-28 22:53:19</th>\n",
       "      <td>22</td>\n",
       "      <td>4000.22</td>\n",
       "      <td>83.75</td>\n",
       "      <td>356.17</td>\n",
       "      <td>181.0</td>\n",
       "      <td>2019-05-28 22:53:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-29 16:02:20</th>\n",
       "      <td>23</td>\n",
       "      <td>10137.39</td>\n",
       "      <td>96.03</td>\n",
       "      <td>1245.05</td>\n",
       "      <td>440.0</td>\n",
       "      <td>2019-05-29 16:02:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-29 20:31:20</th>\n",
       "      <td>22</td>\n",
       "      <td>8799.29</td>\n",
       "      <td>105.93</td>\n",
       "      <td>2386.80</td>\n",
       "      <td>399.0</td>\n",
       "      <td>2019-05-29 20:31:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-29 21:12:20</th>\n",
       "      <td>21</td>\n",
       "      <td>4702.18</td>\n",
       "      <td>97.59</td>\n",
       "      <td>699.19</td>\n",
       "      <td>223.0</td>\n",
       "      <td>2019-05-29 21:12:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-29 21:34:20</th>\n",
       "      <td>24</td>\n",
       "      <td>5368.32</td>\n",
       "      <td>73.77</td>\n",
       "      <td>742.53</td>\n",
       "      <td>223.0</td>\n",
       "      <td>2019-05-29 21:34:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-29 22:46:20</th>\n",
       "      <td>21</td>\n",
       "      <td>6892.93</td>\n",
       "      <td>137.39</td>\n",
       "      <td>1309.64</td>\n",
       "      <td>328.0</td>\n",
       "      <td>2019-05-29 22:46:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-29 23:02:20</th>\n",
       "      <td>24</td>\n",
       "      <td>6331.52</td>\n",
       "      <td>103.16</td>\n",
       "      <td>1196.49</td>\n",
       "      <td>263.0</td>\n",
       "      <td>2019-05-29 23:02:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 20:02:21</th>\n",
       "      <td>24</td>\n",
       "      <td>5038.76</td>\n",
       "      <td>95.34</td>\n",
       "      <td>445.75</td>\n",
       "      <td>209.0</td>\n",
       "      <td>2019-05-30 20:02:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 20:16:21</th>\n",
       "      <td>26</td>\n",
       "      <td>6415.77</td>\n",
       "      <td>85.31</td>\n",
       "      <td>860.74</td>\n",
       "      <td>246.0</td>\n",
       "      <td>2019-05-30 20:16:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:17:21</th>\n",
       "      <td>23</td>\n",
       "      <td>4954.28</td>\n",
       "      <td>97.52</td>\n",
       "      <td>427.05</td>\n",
       "      <td>215.0</td>\n",
       "      <td>2019-05-30 21:17:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:24:21</th>\n",
       "      <td>21</td>\n",
       "      <td>3977.18</td>\n",
       "      <td>93.16</td>\n",
       "      <td>383.06</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2019-05-30 21:24:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:28:21</th>\n",
       "      <td>25</td>\n",
       "      <td>8782.18</td>\n",
       "      <td>98.49</td>\n",
       "      <td>2549.79</td>\n",
       "      <td>351.0</td>\n",
       "      <td>2019-05-30 21:28:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:33:21</th>\n",
       "      <td>27</td>\n",
       "      <td>6456.64</td>\n",
       "      <td>99.65</td>\n",
       "      <td>978.91</td>\n",
       "      <td>239.0</td>\n",
       "      <td>2019-05-30 21:33:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:43:21</th>\n",
       "      <td>21</td>\n",
       "      <td>6371.84</td>\n",
       "      <td>65.98</td>\n",
       "      <td>1175.37</td>\n",
       "      <td>303.0</td>\n",
       "      <td>2019-05-30 21:43:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:47:21</th>\n",
       "      <td>21</td>\n",
       "      <td>3992.83</td>\n",
       "      <td>87.83</td>\n",
       "      <td>440.88</td>\n",
       "      <td>190.0</td>\n",
       "      <td>2019-05-30 21:47:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 21:53:21</th>\n",
       "      <td>24</td>\n",
       "      <td>8467.02</td>\n",
       "      <td>120.22</td>\n",
       "      <td>1511.17</td>\n",
       "      <td>352.0</td>\n",
       "      <td>2019-05-30 21:53:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-30 22:17:21</th>\n",
       "      <td>21</td>\n",
       "      <td>4926.35</td>\n",
       "      <td>85.01</td>\n",
       "      <td>826.90</td>\n",
       "      <td>234.0</td>\n",
       "      <td>2019-05-30 22:17:21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>746 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "create_at                                                              \n",
       "2018-11-01 20:47:09     21       3117.20         84.90        260.82   \n",
       "2018-11-01 21:03:09     21       3706.20         78.12        321.47   \n",
       "2018-11-01 21:13:09     24       4602.03         76.31        391.12   \n",
       "2018-11-02 21:34:11     30       4610.15         72.49        463.41   \n",
       "2018-11-03 14:20:13     21       3113.93         74.29        266.20   \n",
       "2018-11-03 20:16:13     21       2992.24         86.28        246.71   \n",
       "2018-11-03 22:01:13     22       3615.11        108.00        231.49   \n",
       "2018-11-03 22:42:13     28       4332.65         76.26        263.33   \n",
       "2018-11-05 15:49:17     24       3723.64         88.97        280.92   \n",
       "2018-11-05 19:33:17     21       2831.71         78.66        170.69   \n",
       "2018-11-06 20:49:20     21       3414.39         87.02        257.39   \n",
       "2018-11-08 15:56:23     21       3356.42         85.43        252.38   \n",
       "2018-11-08 20:50:23     23       3998.72         90.64        398.60   \n",
       "2018-11-08 20:51:23     21       3736.10         87.71        327.77   \n",
       "2018-11-08 20:59:23     21       3161.50         89.86        423.33   \n",
       "2018-11-09 20:49:25     21       3962.84        129.44        322.40   \n",
       "2018-11-09 21:41:25     21       3199.91         75.82        276.96   \n",
       "2018-11-09 22:09:25     22       3582.53        108.02        246.32   \n",
       "2018-11-10 20:07:26     22       3362.64         80.28        225.21   \n",
       "2018-11-10 21:17:26     21       3407.67        100.55        263.82   \n",
       "2018-11-10 21:48:26     21       3274.11         84.12        354.66   \n",
       "2018-11-10 22:03:26     21       3525.31        119.81        283.33   \n",
       "2018-11-11 17:02:28     21       3123.46         68.51        359.94   \n",
       "2018-11-11 20:45:28     21       3515.21         85.81        297.33   \n",
       "2018-11-11 20:48:28     21       3006.97         83.48        353.50   \n",
       "2018-11-11 22:17:28     23       3709.56         92.62        314.90   \n",
       "2018-11-12 16:28:30     22       3328.76         78.25        257.35   \n",
       "2018-11-12 21:01:30     21       3177.52         92.07        226.59   \n",
       "2018-11-12 21:06:30     21       3887.31        100.05        292.41   \n",
       "2018-11-13 15:51:32     23       3505.80         78.76        249.86   \n",
       "...                    ...           ...           ...           ...   \n",
       "2019-05-27 16:08:18     27      13177.00         80.89       2768.33   \n",
       "2019-05-27 18:29:18     23       5264.64         90.01        515.05   \n",
       "2019-05-27 19:28:18     21       4612.10         93.98        372.50   \n",
       "2019-05-27 19:49:18     28       5647.21         78.28        648.65   \n",
       "2019-05-27 20:03:18     21       5146.42         97.18       1250.87   \n",
       "2019-05-27 20:05:18     21       5242.64        113.51        507.65   \n",
       "2019-05-27 21:13:18     26       4656.33        102.24        300.69   \n",
       "2019-05-27 21:16:18     24       5160.23         95.19        538.70   \n",
       "2019-05-27 21:58:18     25       9587.37         97.71       1304.84   \n",
       "2019-05-27 22:01:18     21       5813.94        118.05       1130.25   \n",
       "2019-05-28 16:19:19     24       5168.07         94.52        869.76   \n",
       "2019-05-28 20:51:19     23       7090.56         89.50       1613.17   \n",
       "2019-05-28 20:52:19     23       5801.02         77.39        802.72   \n",
       "2019-05-28 22:53:19     22       4000.22         83.75        356.17   \n",
       "2019-05-29 16:02:20     23      10137.39         96.03       1245.05   \n",
       "2019-05-29 20:31:20     22       8799.29        105.93       2386.80   \n",
       "2019-05-29 21:12:20     21       4702.18         97.59        699.19   \n",
       "2019-05-29 21:34:20     24       5368.32         73.77        742.53   \n",
       "2019-05-29 22:46:20     21       6892.93        137.39       1309.64   \n",
       "2019-05-29 23:02:20     24       6331.52        103.16       1196.49   \n",
       "2019-05-30 20:02:21     24       5038.76         95.34        445.75   \n",
       "2019-05-30 20:16:21     26       6415.77         85.31        860.74   \n",
       "2019-05-30 21:17:21     23       4954.28         97.52        427.05   \n",
       "2019-05-30 21:24:21     21       3977.18         93.16        383.06   \n",
       "2019-05-30 21:28:21     25       8782.18         98.49       2549.79   \n",
       "2019-05-30 21:33:21     27       6456.64         99.65        978.91   \n",
       "2019-05-30 21:43:21     21       6371.84         65.98       1175.37   \n",
       "2019-05-30 21:47:21     21       3992.83         87.83        440.88   \n",
       "2019-05-30 21:53:21     24       8467.02        120.22       1511.17   \n",
       "2019-05-30 22:17:21     21       4926.35         85.01        826.90   \n",
       "\n",
       "                     res_time_avg            create_at  \n",
       "create_at                                               \n",
       "2018-11-01 20:47:09         148.0  2018-11-01 20:47:09  \n",
       "2018-11-01 21:03:09         176.0  2018-11-01 21:03:09  \n",
       "2018-11-01 21:13:09         191.0  2018-11-01 21:13:09  \n",
       "2018-11-02 21:34:11         153.0  2018-11-02 21:34:11  \n",
       "2018-11-03 14:20:13         148.0  2018-11-03 14:20:13  \n",
       "2018-11-03 20:16:13         142.0  2018-11-03 20:16:13  \n",
       "2018-11-03 22:01:13         164.0  2018-11-03 22:01:13  \n",
       "2018-11-03 22:42:13         154.0  2018-11-03 22:42:13  \n",
       "2018-11-05 15:49:17         155.0  2018-11-05 15:49:17  \n",
       "2018-11-05 19:33:17         134.0  2018-11-05 19:33:17  \n",
       "2018-11-06 20:49:20         162.0  2018-11-06 20:49:20  \n",
       "2018-11-08 15:56:23         159.0  2018-11-08 15:56:23  \n",
       "2018-11-08 20:50:23         173.0  2018-11-08 20:50:23  \n",
       "2018-11-08 20:51:23         177.0  2018-11-08 20:51:23  \n",
       "2018-11-08 20:59:23         150.0  2018-11-08 20:59:23  \n",
       "2018-11-09 20:49:25         188.0  2018-11-09 20:49:25  \n",
       "2018-11-09 21:41:25         152.0  2018-11-09 21:41:25  \n",
       "2018-11-09 22:09:25         162.0  2018-11-09 22:09:25  \n",
       "2018-11-10 20:07:26         152.0  2018-11-10 20:07:26  \n",
       "2018-11-10 21:17:26         162.0  2018-11-10 21:17:26  \n",
       "2018-11-10 21:48:26         155.0  2018-11-10 21:48:26  \n",
       "2018-11-10 22:03:26         167.0  2018-11-10 22:03:26  \n",
       "2018-11-11 17:02:28         148.0  2018-11-11 17:02:28  \n",
       "2018-11-11 20:45:28         167.0  2018-11-11 20:45:28  \n",
       "2018-11-11 20:48:28         143.0  2018-11-11 20:48:28  \n",
       "2018-11-11 22:17:28         161.0  2018-11-11 22:17:28  \n",
       "2018-11-12 16:28:30         151.0  2018-11-12 16:28:30  \n",
       "2018-11-12 21:01:30         151.0  2018-11-12 21:01:30  \n",
       "2018-11-12 21:06:30         185.0  2018-11-12 21:06:30  \n",
       "2018-11-13 15:51:32         152.0  2018-11-13 15:51:32  \n",
       "...                           ...                  ...  \n",
       "2019-05-27 16:08:18         488.0  2019-05-27 16:08:18  \n",
       "2019-05-27 18:29:18         228.0  2019-05-27 18:29:18  \n",
       "2019-05-27 19:28:18         219.0  2019-05-27 19:28:18  \n",
       "2019-05-27 19:49:18         201.0  2019-05-27 19:49:18  \n",
       "2019-05-27 20:03:18         245.0  2019-05-27 20:03:18  \n",
       "2019-05-27 20:05:18         249.0  2019-05-27 20:05:18  \n",
       "2019-05-27 21:13:18         179.0  2019-05-27 21:13:18  \n",
       "2019-05-27 21:16:18         215.0  2019-05-27 21:16:18  \n",
       "2019-05-27 21:58:18         383.0  2019-05-27 21:58:18  \n",
       "2019-05-27 22:01:18         276.0  2019-05-27 22:01:18  \n",
       "2019-05-28 16:19:19         215.0  2019-05-28 16:19:19  \n",
       "2019-05-28 20:51:19         308.0  2019-05-28 20:51:19  \n",
       "2019-05-28 20:52:19         252.0  2019-05-28 20:52:19  \n",
       "2019-05-28 22:53:19         181.0  2019-05-28 22:53:19  \n",
       "2019-05-29 16:02:20         440.0  2019-05-29 16:02:20  \n",
       "2019-05-29 20:31:20         399.0  2019-05-29 20:31:20  \n",
       "2019-05-29 21:12:20         223.0  2019-05-29 21:12:20  \n",
       "2019-05-29 21:34:20         223.0  2019-05-29 21:34:20  \n",
       "2019-05-29 22:46:20         328.0  2019-05-29 22:46:20  \n",
       "2019-05-29 23:02:20         263.0  2019-05-29 23:02:20  \n",
       "2019-05-30 20:02:21         209.0  2019-05-30 20:02:21  \n",
       "2019-05-30 20:16:21         246.0  2019-05-30 20:16:21  \n",
       "2019-05-30 21:17:21         215.0  2019-05-30 21:17:21  \n",
       "2019-05-30 21:24:21         189.0  2019-05-30 21:24:21  \n",
       "2019-05-30 21:28:21         351.0  2019-05-30 21:28:21  \n",
       "2019-05-30 21:33:21         239.0  2019-05-30 21:33:21  \n",
       "2019-05-30 21:43:21         303.0  2019-05-30 21:43:21  \n",
       "2019-05-30 21:47:21         190.0  2019-05-30 21:47:21  \n",
       "2019-05-30 21:53:21         352.0  2019-05-30 21:53:21  \n",
       "2019-05-30 22:17:21         234.0  2019-05-30 22:17:21  \n",
       "\n",
       "[746 rows x 6 columns]"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['count'] > 20] # 看一下count > 20 的所有时间，初步认为大于20不是异常，只是高峰时段"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 某一天平均响应时间\n",
    "df['2019-5-1']['res_time_avg'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['2019-5-1'][['res_time_avg']].boxplot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>create_at</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>create_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-05-01 00:34:48</th>\n",
       "      <td>1</td>\n",
       "      <td>1694.47</td>\n",
       "      <td>1694.47</td>\n",
       "      <td>1694.47</td>\n",
       "      <td>1694.0</td>\n",
       "      <td>2019-05-01 00:34:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 14:00:49</th>\n",
       "      <td>17</td>\n",
       "      <td>19770.18</td>\n",
       "      <td>207.54</td>\n",
       "      <td>2974.52</td>\n",
       "      <td>1162.0</td>\n",
       "      <td>2019-05-01 14:00:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 18:36:49</th>\n",
       "      <td>8</td>\n",
       "      <td>8799.92</td>\n",
       "      <td>96.59</td>\n",
       "      <td>3233.26</td>\n",
       "      <td>1099.0</td>\n",
       "      <td>2019-05-01 18:36:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 19:09:49</th>\n",
       "      <td>6</td>\n",
       "      <td>7399.94</td>\n",
       "      <td>307.39</td>\n",
       "      <td>3153.02</td>\n",
       "      <td>1233.0</td>\n",
       "      <td>2019-05-01 19:09:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 19:10:49</th>\n",
       "      <td>13</td>\n",
       "      <td>23595.60</td>\n",
       "      <td>206.20</td>\n",
       "      <td>4664.84</td>\n",
       "      <td>1815.0</td>\n",
       "      <td>2019-05-01 19:10:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01 20:38:49</th>\n",
       "      <td>15</td>\n",
       "      <td>16169.25</td>\n",
       "      <td>142.47</td>\n",
       "      <td>3624.26</td>\n",
       "      <td>1077.0</td>\n",
       "      <td>2019-05-01 20:38:49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "create_at                                                              \n",
       "2019-05-01 00:34:48      1       1694.47       1694.47       1694.47   \n",
       "2019-05-01 14:00:49     17      19770.18        207.54       2974.52   \n",
       "2019-05-01 18:36:49      8       8799.92         96.59       3233.26   \n",
       "2019-05-01 19:09:49      6       7399.94        307.39       3153.02   \n",
       "2019-05-01 19:10:49     13      23595.60        206.20       4664.84   \n",
       "2019-05-01 20:38:49     15      16169.25        142.47       3624.26   \n",
       "\n",
       "                     res_time_avg            create_at  \n",
       "create_at                                               \n",
       "2019-05-01 00:34:48        1694.0  2019-05-01 00:34:48  \n",
       "2019-05-01 14:00:49        1162.0  2019-05-01 14:00:49  \n",
       "2019-05-01 18:36:49        1099.0  2019-05-01 18:36:49  \n",
       "2019-05-01 19:09:49        1233.0  2019-05-01 19:09:49  \n",
       "2019-05-01 19:10:49        1815.0  2019-05-01 19:10:49  \n",
       "2019-05-01 20:38:49        1077.0  2019-05-01 20:38:49  "
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df['2019-5-1']\n",
    "df2[df2['res_time_avg'] > 1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['2019-5-1'][['res_time_sum', 'res_time_min', 'res_time_max', 'res_time_avg']].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = df['2019-5-1'].resample('20T').mean() # 以20分钟重新采样\n",
    "data[['res_time_sum', 'res_time_min', 'res_time_max', 'res_time_avg']].plot()\n",
    "plt.show()\n",
    "# 可看出业务高峰时段 下午2-3点，晚上7-8点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['2019-5-1' : '2019-5-10']['count'].plot()\n",
    "plt.show()\n",
    "# 可以看出这十天的情况都差不多"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "            ...\n",
       "            2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n",
       "           dtype='int64', name='create_at', length=884)"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 看下周末的情况\n",
    "df['2019-5-1'].index.weekday # 索引是时间对象，其属性weekday可看出是周几，从0开始编号 0：周一,..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>create_at</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>create_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:07</th>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:01:07</th>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:02:07</th>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:03:07</th>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:04:07</th>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "create_at                                                              \n",
       "2018-11-01 00:00:07      8       1057.31         88.75        177.72   \n",
       "2018-11-01 00:01:07      5        749.12        103.79        240.38   \n",
       "2018-11-01 00:02:07      5        845.84        136.31        225.73   \n",
       "2018-11-01 00:03:07      9       1305.52         90.12        196.61   \n",
       "2018-11-01 00:04:07      3        568.89        138.45        232.02   \n",
       "\n",
       "                     res_time_avg            create_at  weekday  \n",
       "create_at                                                        \n",
       "2018-11-01 00:00:07         132.0  2018-11-01 00:00:07        3  \n",
       "2018-11-01 00:01:07         149.0  2018-11-01 00:01:07        3  \n",
       "2018-11-01 00:02:07         169.0  2018-11-01 00:02:07        3  \n",
       "2018-11-01 00:03:07         145.0  2018-11-01 00:03:07        3  \n",
       "2018-11-01 00:04:07         189.0  2018-11-01 00:04:07        3  "
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['weekday'] = df.index.weekday # 给数据增加一列星期\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>create_at</th>\n",
       "      <th>weekday</th>\n",
       "      <th>weekend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>create_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:07</th>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:01:07</th>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "create_at                                                              \n",
       "2018-11-01 00:00:07      8       1057.31         88.75        177.72   \n",
       "2018-11-01 00:01:07      5        749.12        103.79        240.38   \n",
       "\n",
       "                     res_time_avg            create_at  weekday  weekend  \n",
       "create_at                                                                 \n",
       "2018-11-01 00:00:07         132.0  2018-11-01 00:00:07        3    False  \n",
       "2018-11-01 00:01:07         149.0  2018-11-01 00:01:07        3    False  "
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['weekend'] = df['weekday'].isin({5,6}) # 再增加一列，表示是否周末\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weekend\n",
       "False    7.016846\n",
       "True     7.574989\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对周末进行分组 然后求count列的平均值\n",
    "df.groupby('weekend')['count'].mean()\n",
    "# 可以看出 周末平均多出0.5次"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weekend  create_at\n",
       "False    0             3.239120\n",
       "         1             1.668388\n",
       "         2             1.162551\n",
       "         3             1.086705\n",
       "         4             1.155556\n",
       "         5             1.136364\n",
       "         6             1.000000\n",
       "         7             1.000000\n",
       "         8             1.000000\n",
       "         9             1.080000\n",
       "         10            1.239011\n",
       "         11            2.031690\n",
       "         12            4.195845\n",
       "         13            6.668042\n",
       "         14            8.260503\n",
       "         15            8.934448\n",
       "         16            8.466504\n",
       "         17            6.784996\n",
       "         18            6.717731\n",
       "         19            8.655913\n",
       "         20           10.536496\n",
       "         21           10.846906\n",
       "         22            9.034164\n",
       "         23            5.946834\n",
       "True     0             3.467782\n",
       "         1             1.741849\n",
       "         2             1.161826\n",
       "         3             1.050000\n",
       "         4             1.076923\n",
       "         5             1.333333\n",
       "         6             1.000000\n",
       "         7             1.000000\n",
       "         8             1.071429\n",
       "         9             1.144928\n",
       "         10            1.254111\n",
       "         11            1.992958\n",
       "         12            4.031889\n",
       "         13            6.905772\n",
       "         14            8.851321\n",
       "         15            9.858422\n",
       "         16            9.420550\n",
       "         17            7.334743\n",
       "         18            7.342150\n",
       "         19            9.270430\n",
       "         20           11.173609\n",
       "         21           11.695043\n",
       "         22           10.419916\n",
       "         23            7.025452\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 看看周末与平时 各小时的情况\n",
    "df.groupby(['weekend',df.index.hour])['count'].mean() # 先以周末分组，后以小时分组 最后求count列的均值\n",
    "# 可以看出 14-15点 ，20-21点时 周末的请求次数更高一些"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a78a3c73c8>"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['weekend',df.index.hour])['count'].mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>weekend</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>create_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.239120</td>\n",
       "      <td>3.467782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.668388</td>\n",
       "      <td>1.741849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.162551</td>\n",
       "      <td>1.161826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.086705</td>\n",
       "      <td>1.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.155556</td>\n",
       "      <td>1.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.136364</td>\n",
       "      <td>1.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.071429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.080000</td>\n",
       "      <td>1.144928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.239011</td>\n",
       "      <td>1.254111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.031690</td>\n",
       "      <td>1.992958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.195845</td>\n",
       "      <td>4.031889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6.668042</td>\n",
       "      <td>6.905772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8.260503</td>\n",
       "      <td>8.851321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8.934448</td>\n",
       "      <td>9.858422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>8.466504</td>\n",
       "      <td>9.420550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>6.784996</td>\n",
       "      <td>7.334743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6.717731</td>\n",
       "      <td>7.342150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>8.655913</td>\n",
       "      <td>9.270430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>10.536496</td>\n",
       "      <td>11.173609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>10.846906</td>\n",
       "      <td>11.695043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>9.034164</td>\n",
       "      <td>10.419916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.946834</td>\n",
       "      <td>7.025452</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "weekend        False      True \n",
       "create_at                      \n",
       "0           3.239120   3.467782\n",
       "1           1.668388   1.741849\n",
       "2           1.162551   1.161826\n",
       "3           1.086705   1.050000\n",
       "4           1.155556   1.076923\n",
       "5           1.136364   1.333333\n",
       "6           1.000000   1.000000\n",
       "7           1.000000   1.000000\n",
       "8           1.000000   1.071429\n",
       "9           1.080000   1.144928\n",
       "10          1.239011   1.254111\n",
       "11          2.031690   1.992958\n",
       "12          4.195845   4.031889\n",
       "13          6.668042   6.905772\n",
       "14          8.260503   8.851321\n",
       "15          8.934448   9.858422\n",
       "16          8.466504   9.420550\n",
       "17          6.784996   7.334743\n",
       "18          6.717731   7.342150\n",
       "19          8.655913   9.270430\n",
       "20         10.536496  11.173609\n",
       "21         10.846906  11.695043\n",
       "22          9.034164  10.419916\n",
       "23          5.946834   7.025452"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['weekend',df.index.hour])['count'].mean().unstack(level = 0) # level:0-以竖列形式 1-横向"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a78717feb8>"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 将周末与非周末绘制在一张图上\n",
    "df.groupby(['weekend',df.index.hour])['count'].mean().unstack(level = 0).plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
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